Bump version to 0.2.28

Fix TypeError: 'FastInfo' object is not callable #1636
Merge pull request #1661 from ranaroussi/dev
2023-08-13 13:05:02 +01:00 · 2023-08-13 13:01:53 +01:00 · 2023-08-13 12:44:05 +01:00 · 2023-08-13 12:40:11 +01:00 · 2023-08-13 12:21:55 +01:00 · 2023-08-13 10:54:57 +01:00
53 changed files with 3058 additions and 1638 deletions
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -1,44 +0,0 @@
---
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
---
-
-# IMPORTANT
-
-# Read and follow these instructions carefully. Help us help you.
-
-### Are you up-to-date?
-
-Upgrade to the latest version and confirm the issue/bug is still there.
-
-`$ pip install yfinance --upgrade --no-cache-dir`
-
-Confirm by running:
-
-`import yfinance as yf ; print(yf.__version__)`
-
-and comparing against [PIP](https://pypi.org/project/yfinance/#history).
-
-### Does Yahoo actually have the data?
-
-Are you spelling symbol *exactly* same as Yahoo?
-
-Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
-
-### Are you spamming Yahoo?
-
-Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
-
-### Still think it's a bug?
-
-**Delete these instructions** and replace with your bug report, providing the following as best you can:
-
- Simple code that reproduces your problem, that we can copy-paste-run.
- Run code with [debug logging enabled](https://github.com/ranaroussi/yfinance/tree/dev#logging) and post the full output.
- If you think `yfinance` returning bad data, give us proof.
- `yfinance` version and Python version.
- Operating system type.
--- a/.github/ISSUE_TEMPLATE/bug_report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yaml
@@ -0,0 +1,88 @@
+name: Bug report
+description: Report a bug in our project
+labels: ["bug"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        # IMPORTANT - Read and follow these instructions carefully. Help us help you.
+
+        ### Does issue already exist?
+
+        Use the search tool. Don't annoy everyone by duplicating existing Issues.
+
+        ### Are you up-to-date?
+
+        Upgrade to the latest version and confirm the issue/bug is still there.
+
+        `$ pip install yfinance --upgrade --no-cache-dir`
+
+        Confirm by running:
+
+        `import yfinance as yf ; print(yf.__version__)`
+
+        and comparing against [PIP](https://pypi.org/project/yfinance/#history).
+
+        ### Does Yahoo actually have the data?
+
+        Are you spelling symbol *exactly* same as Yahoo?
+
+        Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
+
+        ### Are you spamming Yahoo?
+
+        Yahoo Finance free service has rate-limiting https://github.com/ranaroussi/yfinance/discussions/1513. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
+        
+  - type: markdown
+    attributes:
+      value: |
+        ---
+        ## Still think it's a bug?
+
+        Provide the following as best you can:
+
+  - type: textarea
+    id: code
+    attributes:
+      label: "Simple code that reproduces your problem"
+      description: "Provide a snippet of code that we can copy-paste-run. Wrap code in Python Markdown code blocks for proper formatting (```` ```python ... ``` ````)."
+    validations:
+      required: true
+
+  - type: textarea
+    id: debug-log
+    attributes:
+      label: "Debug log"
+      description: "Run code with debug logging enabled and post the full output. Instructions: https://github.com/ranaroussi/yfinance/tree/main#logging"
+    validations:
+      required: true
+
+  - type: textarea
+    id: bad-data-proof
+    attributes:
+      label: "Bad data proof"
+      description: "If you think `yfinance` returning bad data, provide your proof here."
+    validations:
+      required: false
+
+  - type: input
+    id: version-yfinance
+    attributes:
+      label: "`yfinance` version"
+    validations:
+      required: true
+
+  - type: input
+    id: version-python
+    attributes:
+      label: "Python version"
+    validations:
+      required: false
+
+  - type: input
+    id: os
+    attributes:
+      label: "Operating system"
+    validations:
+      required: false
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -13,9 +13,9 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
    - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
      with:
        python-version: '3.x'
    - name: Install dependencies
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@ dist
 yfinance.egg-info
 *.pyc
 .coverage
+.idea/
 .vscode/
 build/
 *.html
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,7 +1,49 @@
 Change Log
 ===========

-0.2.19
+0.2.28
+------
+- Fix TypeError: 'FastInfo' object is not callable #1636
+- Improve & fix price repair #1633 #1660
+- option_chain() also return underlying data #1606
+
+0.2.27
+------
+Bug fixes:
+- fix merging 1d-prices with out-of-range divs/splits #1635
+- fix multithread error 'tz already in cache' #1648
+
+0.2.26
+------
+Proxy improvements
+- bug fixes #1371
+- security fix #1625
+
+0.2.25
+------
+Fix single ISIN as ticker #1611
+Fix 'Only 100 years allowed' error #1576
+
+0.2.24
+------
+Fix info[] missing values #1603
+
+0.2.23
+------
+Fix 'Unauthorized' error #1595
+
+0.2.22
+------
+Fix unhandled 'sqlite3.DatabaseError' #1574
+
+0.2.21
+------
+Fix financials tables #1568
+Price repair update: fix Yahoo messing up dividend and split adjustments #1543
+Fix logging behaviour #1562
+Fix merge future div/split into prices #1567
+
+0.2.20
 ------
 Switch to `logging` module #1493 #1522 #1541
 Price history:
--- a/README.md
+++ b/README.md
@@ -42,20 +42,6 @@ Yahoo! finance API is intended for personal use only.**

 ---

-## News
-
-### 2023-01-27
-Since December 2022 Yahoo has been encrypting the web data that `yfinance` scrapes for non-price data. Price data still works. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence `yfinance` breaking several times. `yfinance` is now better prepared for any future changes by Yahoo.
-
-Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. Then in 0.2.6 introduced `Ticker.fast_info`, providing much faster access to some `Ticker.info` elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary).
-
-### 2023-02-07
-Yahoo is now regularly changing their decryption key, breaking `yfinance` decryption. Is technically possible to extract this from their webpage but not implemented because difficult, see [discussion in the issue thread](https://github.com/ranaroussi/yfinance/issues/1407).
-
-### 2023-04-09
-
-Fixed `Ticker.info`
-
 ## Quick Start

 ### The Ticker module
@@ -83,9 +69,6 @@ msft.splits
 msft.capital_gains  # only for mutual funds & etfs

 # show share count
-# - yearly summary:
-msft.shares
-# - accurate time-series count:
 msft.get_shares_full(start="2022-01-01", end=None)

 # show financials:
@@ -105,25 +88,6 @@ msft.major_holders
 msft.institutional_holders
 msft.mutualfund_holders

-# show earnings
-msft.earnings
-msft.quarterly_earnings
-
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-msft.recommendations_summary
-# show analysts other work
-msft.analyst_price_target
-msft.revenue_forecasts
-msft.earnings_forecasts
-msft.earnings_trend
-
-# show next event (earnings, etc)
-msft.calendar
-
 # Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. 
 # Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
 msft.earnings_dates
@@ -180,31 +144,14 @@ To download price history into one table:

 ```python
 import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
+data = yf.download("SPY AAPL", period="1mo")
 ```

-`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
-
-```python
-yf.download(tickers = "SPY AAPL",  # list of tickers
-            period = "1y",         # time period
-            interval = "1d",       # trading interval
-            prepost = False,       # download pre/post market hours data?
-            repair = True)         # repair obvious price errors e.g. 100x?
-```
-
-Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.

 ### Logging

-`yfinance` now uses the `logging` module. To control the detail of printed messages you simply change the level:
-```
-import logging
-logger = logging.getLogger('yfinance')
-logger.setLevel(logging.ERROR)  # default: only print errors
-logger.setLevel(logging.CRITICAL)  # disable printing
-logger.setLevel(logging.DEBUG)  # verbose: print errors & debug info
-```
+`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.

 ### Smarter scraping

@@ -231,7 +178,7 @@ class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
    pass

 session = CachedLimiterSession(
-    limiter=Limiter(RequestRate(2, Duration.SECOND*5),  # max 2 requests per 5 seconds
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
    bucket_class=MemoryQueueBucket,
    backend=SQLiteCache("yfinance.cache"),
 )
@@ -304,14 +251,13 @@ To install `yfinance` using `conda`, see
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
 -   [Pandas](https://github.com/pydata/pandas) \>= 1.3.0
 -   [Numpy](http://www.numpy.org) \>= 1.16.5
-   [requests](http://docs.python-requests.org/en/master) \>= 2.26
+-   [requests](http://docs.python-requests.org/en/master) \>= 2.31
 -   [lxml](https://pypi.org/project/lxml) \>= 4.9.1
 -   [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
 -   [pytz](https://pypi.org/project/pytz) \>=2022.5
 -   [frozendict](https://pypi.org/project/frozendict) \>= 2.3.4
 -   [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
 -   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
-   [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2

 #### Optional (if you want to use `pandas_datareader`)

--- a/meta.yaml
+++ b/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.19" %}
+{% set version = "0.2.28" %}

 package:
  name: "{{ name|lower }}"
@@ -18,7 +18,7 @@ requirements:
  host:
    - pandas >=1.3.0
    - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
    - multitasking >=0.0.7
    - lxml >=4.9.1
    - appdirs >=1.4.4
@@ -27,14 +27,13 @@ requirements:
    - beautifulsoup4 >=4.11.1
    - html5lib >=1.1
    # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
    - pip
    - python

  run:
    - pandas >=1.3.0
    - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
    - multitasking >=0.0.7
    - lxml >=4.9.1
    - appdirs >=1.4.4
@@ -43,7 +42,6 @@ requirements:
    - beautifulsoup4 >=4.11.1
    - html5lib >=1.1
    # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
    - python

 test:
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.26
+requests>=2.31
 multitasking>=0.0.7
 lxml>=4.9.1
 appdirs>=1.4.4
@@ -8,4 +8,3 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
-cryptography>=3.3.2
--- a/setup.py
+++ b/setup.py
@@ -39,7 +39,7 @@ setup(
        'License :: OSI Approved :: Apache Software License',
        # 'Development Status :: 3 - Alpha',
        'Development Status :: 4 - Beta',
-        #'Development Status :: 5 - Production/Stable',
+        # 'Development Status :: 5 - Production/Stable',


        'Operating System :: OS Independent',
@@ -60,12 +60,11 @@ setup(
    keywords='pandas, yahoo finance, pandas datareader',
    packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
-                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'requests>=2.31', 'multitasking>=0.0.7',
                      'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
                      'frozendict>=2.3.4', 
-                      # 'pycryptodome>=3.6.6',
-                      'cryptography>=3.3.2',
                      'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
+    # Note: Pandas.read_html() needs html5lib & beautifulsoup4
    entry_points={
        'console_scripts': [
            'sample=sample:main',
--- a/tests/context.py
+++ b/tests/context.py
@@ -26,11 +26,16 @@ class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
 from pyrate_limiter import Duration, RequestRate, Limiter
 history_rate = RequestRate(1, Duration.SECOND*2)
 limiter = Limiter(history_rate)
+cache_fp = os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache")
+if os.path.isfile(cache_fp + '.sqlite'):
+    # Delete local cache if older than 1 day:
+    mod_dt = _dt.datetime.fromtimestamp(os.path.getmtime(cache_fp + '.sqlite'))
+    if mod_dt.date() < _dt.date.today():
+        os.remove(cache_fp + '.sqlite')
 session_gbl = CachedLimiterSession(
    limiter=limiter,
    bucket_class=MemoryQueueBucket,
-    backend=SQLiteCache(os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache"), 
-    					expire_after=_dt.timedelta(hours=1)),
+    backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
 )
 # Use this instead if only want rate-limiting:
 # from requests_ratelimiter import LimiterSession
--- a/tests/data/4063-T-1d-bad-stock-split-fixed.csv
+++ b/tests/data/4063-T-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,4200,4207,4124,4142,4087.837109375,4505500,0,0
+2023-03-27 00:00:00+09:00,4196,4204,4151,4192,4137.183203125,5959500,0,0
+2023-03-24 00:00:00+09:00,4130,4187,4123,4177,4122.379296875,8961500,0,0
+2023-03-23 00:00:00+09:00,4056,4106,4039,4086,4032.569140625,5480000,0,0
+2023-03-22 00:00:00+09:00,4066,4128,4057,4122,4068.0984375,8741500,0,0
+2023-03-20 00:00:00+09:00,4000,4027,3980,3980,3927.95546875,7006500,0,0
+2023-03-17 00:00:00+09:00,4018,4055,4016,4031,3978.28828125,6961500,0,0
+2023-03-16 00:00:00+09:00,3976,4045,3972,4035,3982.236328125,5019000,0,0
+2023-03-15 00:00:00+09:00,4034,4050,4003,4041,3988.1578125,6122000,0,0
--- a/tests/data/4063-T-1d-bad-stock-split.csv
+++ b/tests/data/4063-T-1d-bad-stock-split.csv
@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
+2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
+2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
+2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
+2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
+2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
+2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
+2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
+2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0
--- a/tests/data/8TRA-DE-1d-missing-div-adjust-fixed.csv
+++ b/tests/data/8TRA-DE-1d-missing-div-adjust-fixed.csv
@@ -0,0 +1,6 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-30 00:00:00+02:00,19.5900001525879,19.7999992370605,19.2700004577637,19.3500003814697,18.6291382416581,196309,0,0
+2023-05-31 00:00:00+02:00,19.1200008392334,19.1399993896484,18.7000007629395,18.7900009155273,18.0900009155273,156652,0,0
+2023-06-02 00:00:00+02:00,18.5499992370605,19,18.5100002288818,18.8999996185303,18.8999996185303,83439,0.7,0
+2023-06-05 00:00:00+02:00,18.9300003051758,19.0900001525879,18.8400001525879,19,19,153167,0,0
+2023-06-06 00:00:00+02:00,18.9099998474121,18.9500007629395,18.5100002288818,18.6599998474121,18.6599998474121,104352,0,0
--- a/tests/data/8TRA-DE-1d-missing-div-adjust.csv
+++ b/tests/data/8TRA-DE-1d-missing-div-adjust.csv
@@ -0,0 +1,6 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-30 00:00:00+02:00,19.59000015258789,19.799999237060547,19.270000457763672,19.350000381469727,19.350000381469727,196309,0.0,0.0
+2023-05-31 00:00:00+02:00,19.1200008392334,19.139999389648438,18.700000762939453,18.790000915527344,18.790000915527344,156652,0.0,0.0
+2023-06-02 00:00:00+02:00,18.549999237060547,19.0,18.510000228881836,18.899999618530273,18.899999618530273,83439,0.7,0.0
+2023-06-05 00:00:00+02:00,18.93000030517578,19.09000015258789,18.84000015258789,19.0,19.0,153167,0.0,0.0
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--- a/tests/data/AET-L-1d-100x-error-fixed.csv
+++ b/tests/data/AET-L-1d-100x-error-fixed.csv
@@ -0,0 +1,24 @@
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--- a/tests/data/AET-L-1d-100x-error.csv
+++ b/tests/data/AET-L-1d-100x-error.csv
@@ -0,0 +1,24 @@
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--- a/tests/data/AET-L-1wk-100x-error-fixed.csv
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv
@@ -0,0 +1,37 @@
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--- a/tests/data/AET-L-1wk-100x-error-fixed.csv.old
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv.old
@@ -0,0 +1,25 @@
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--- a/tests/data/AET-L-1wk-100x-error.csv
+++ b/tests/data/AET-L-1wk-100x-error.csv
@@ -0,0 +1,37 @@
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+2021-09-27 00:00:00+01:00,15.600000381469727,16.0,14.899999618530273,15.050000190734863,15.050000190734863,3860549,0.0,0.0
--- a/tests/data/AET-L-1wk-100x-error.csv.old
+++ b/tests/data/AET-L-1wk-100x-error.csv.old
@@ -0,0 +1,25 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv
+++ b/tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/ALPHA-PA-1d-bad-stock-split.csv
+++ b/tests/data/ALPHA-PA-1d-bad-stock-split.csv
@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/AV-L-1wk-bad-stock-split-fixed.csv
+++ b/tests/data/AV-L-1wk-bad-stock-split-fixed.csv
@@ -0,0 +1,85 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/AV-L-1wk-bad-stock-split.csv
+++ b/tests/data/AV-L-1wk-bad-stock-split.csv
@@ -0,0 +1,85 @@
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--- a/tests/data/CNE-L-1d-bad-stock-split-fixed.csv
+++ b/tests/data/CNE-L-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,11 @@
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--- a/tests/data/CNE-L-1d-bad-stock-split.csv
+++ b/tests/data/CNE-L-1d-bad-stock-split.csv
@@ -0,0 +1,11 @@
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--- a/tests/data/DEX-AX-1d-bad-stock-split-fixed.csv
+++ b/tests/data/DEX-AX-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,24 @@
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--- a/tests/data/DEX-AX-1d-bad-stock-split.csv
+++ b/tests/data/DEX-AX-1d-bad-stock-split.csv
@@ -0,0 +1,24 @@
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--- a/tests/data/LA-V-1d-bad-stock-split-fixed.csv
+++ b/tests/data/LA-V-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,42 @@
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--- a/tests/data/LA-V-1d-bad-stock-split.csv
+++ b/tests/data/LA-V-1d-bad-stock-split.csv
@@ -0,0 +1,42 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/MOB-ST-1d-bad-stock-split-fixed.csv
+++ b/tests/data/MOB-ST-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,17 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/MOB-ST-1d-bad-stock-split.csv
+++ b/tests/data/MOB-ST-1d-bad-stock-split.csv
@@ -0,0 +1,17 @@
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--- a/tests/data/SPM-MI-1d-bad-stock-split-fixed.csv
+++ b/tests/data/SPM-MI-1d-bad-stock-split-fixed.csv
@@ -0,0 +1,23 @@
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--- a/tests/data/SPM-MI-1d-bad-stock-split.csv
+++ b/tests/data/SPM-MI-1d-bad-stock-split.csv
@@ -0,0 +1,23 @@
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+2022-06-09 00:00:00+02:00,50.97999954223633,50.97999954223633,48.7599983215332,48.939998626708984,48.939998626708984,202548,0.0,0.0
+2022-06-10 00:00:00+02:00,48.79999923706055,48.79999923706055,45.040000915527344,45.040000915527344,45.040000915527344,298273,0.0,0.0
+2022-06-13 00:00:00+02:00,43.0,43.7599983215332,38.36000061035156,38.36000061035156,38.36000061035156,456821,0.0,0.1
+2022-06-14 00:00:00+02:00,38.775001525878906,41.599998474121094,38.52000045776367,39.439998626708984,39.439998626708984,535450,0.0,0.0
+2022-06-15 00:00:00+02:00,40.34000015258789,41.64500045776367,37.30500030517578,37.30500030517578,37.30500030517578,666261,0.0,0.0
+2022-06-16 00:00:00+02:00,37.30500030517578,39.849998474121094,37.2400016784668,38.255001068115234,38.255001068115234,1337996,0.0,0.0
+2022-06-17 00:00:00+02:00,38.0,42.994998931884766,37.5,42.994998931884766,42.994998931884766,1284416,0.0,0.0
+2022-06-20 00:00:00+02:00,2.1942219734191895,2.2295401096343994,2.139925956726074,2.2295401096343994,2.2295401096343994,12364104,0.0,0.0
+2022-06-21 00:00:00+02:00,2.247199058532715,2.2851529121398926,2.1971209049224854,2.2155709266662598,2.2155709266662598,8434013,0.0,0.0
+2022-06-22 00:00:00+02:00,1.986791968345642,2.003659963607788,1.7379820346832275,1.7379820346832275,1.7379820346832275,26496542,0.0,0.0
+2022-06-23 00:00:00+02:00,1.6241190433502197,1.6852680444717407,1.3732000589370728,1.5977619886398315,1.5977619886398315,48720201,0.0,0.0
+2022-06-24 00:00:00+02:00,1.475993037223816,1.5461030006408691,1.1739410161972046,1.2493220567703247,1.2493220567703247,56877192,0.0,0.0
+2022-06-27 00:00:00+02:00,1.4989999532699585,1.7984999418258667,1.4989999532699585,1.7984999418258667,1.7984999418258667,460673,0.0,0.0
+2022-06-28 00:00:00+02:00,2.1579999923706055,3.0510001182556152,2.125999927520752,3.0510001182556152,3.0510001182556152,3058635,0.0,0.0
+2022-06-29 00:00:00+02:00,2.9000000953674316,3.73799991607666,2.8589999675750732,3.2639999389648438,3.2639999389648438,6516761,0.0,0.0
+2022-06-30 00:00:00+02:00,3.249000072479248,3.2809998989105225,2.5,2.555000066757202,2.555000066757202,4805984,0.0,0.0
--- a/tests/data/SSW-JO-1d-100x-error-fixed.csv
+++ b/tests/data/SSW-JO-1d-100x-error-fixed.csv
@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.7000,34.7100,33.2400,33.6200,33.6200,7148409,0,0
+2023-06-08 00:00:00+02:00,34.9000,34.9900,34.0400,34.3600,34.3600,10406999,0,0
+2023-06-07 00:00:00+02:00,34.5500,35.6400,34.3200,35.0900,35.0900,10118918,0,0
+2023-06-06 00:00:00+02:00,34.5000,34.8200,34.0500,34.4600,34.4600,9109709,0,0
+2023-06-05 00:00:00+02:00,35.0000,35.3000,34.2000,34.7000,34.7000,8791993,0,0
+2023-06-02 00:00:00+02:00,35.6900,36.1800,34.6000,34.9700,34.9700,8844549,0,0
+2023-06-01 00:00:00+02:00,35.2300,35.3800,34.2400,35.3500,35.3500,6721030,0,0
+2023-05-31 00:00:00+02:00,34.8,35.48,34.26,35.01,35.01,32605833,0,0
+2023-05-30 00:00:00+02:00,34.39,35.37,33.85,34.23,34.23,8970804,0,0
+2023-05-29 00:00:00+02:00,34.66,35.06,34.02,34.32,34.32,3912803,0,0
+2023-05-26 00:00:00+02:00,34.75,35.99,34.33,34.53,34.53,6744718,0,0
+2023-05-25 00:00:00+02:00,35.4,36.09,34.63,35.07,35.07,16900221,0,0
+2023-05-24 00:00:00+02:00,36.2,36.5,35.26,35.4,35.4,9049505,0,0
+2023-05-23 00:00:00+02:00,36.9,36.67,35.56,36.1,36.1,10797373,0,0
+2023-05-22 00:00:00+02:00,37.05,37.36,36.09,36.61,36.61,7132641,0,0
+2023-05-19 00:00:00+02:00,36.2,37.15,36.25,36.9,36.9,12648518,0,0
+2023-05-18 00:00:00+02:00,36.57,36.99,35.84,36.46,36.46,10674542,0,0
+2023-05-17 00:00:00+02:00,36.87,37.31,36.56,36.71,36.71,9892791,0,0
+2023-05-16 00:00:00+02:00,37.15,37.73,36.96,37.03,37.03,4706789,0,0
+2023-05-15 00:00:00+02:00,37.74,38.05,36.96,37.27,37.27,7890969,0,0
+2023-05-12 00:00:00+02:00,37.5,38.44,36.71,37.74,37.74,8724303,0,0
+2023-05-11 00:00:00+02:00,38.8,38.88,37.01,37.32,37.32,14371855,0,0
+2023-05-10 00:00:00+02:00,38.93,38.8,36.42,38.1,38.1,30393389,0,0
+2023-05-09 00:00:00+02:00,44.41,44.41,39.39,39.66,39.66,19833428,0,0
+2023-05-08 00:00:00+02:00,44.63,45.78,44.56,44.71,44.71,11092519,0,0
+2023-05-05 00:00:00+02:00,42.99,44.9,42.87,44.58,44.58,28539048,0,0
+2023-05-04 00:00:00+02:00,41.49,43.3,41.23,42.83,42.83,15506868,0,0
+2023-05-03 00:00:00+02:00,39.75,40.98,39.68,40.95,40.95,14657028,0,0
+2023-05-02 00:00:00+02:00,40.37,40.32,39.17,39.65,39.65,11818133,0,0
--- a/tests/data/SSW-JO-1d-100x-error.csv
+++ b/tests/data/SSW-JO-1d-100x-error.csv
@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.700001,34.709999,33.240002,33.619999,33.619999,7148409,0,0
+2023-06-08 00:00:00+02:00,34.900002,34.990002,34.040001,34.360001,34.360001,10406999,0,0
+2023-06-07 00:00:00+02:00,34.549999,35.639999,34.320000,35.090000,35.090000,10118918,0,0
+2023-06-06 00:00:00+02:00,34.500000,34.820000,34.049999,34.459999,34.459999,9109709,0,0
+2023-06-05 00:00:00+02:00,35.000000,35.299999,34.200001,34.700001,34.700001,8791993,0,0
+2023-06-02 00:00:00+02:00,35.689999,36.180000,34.599998,34.970001,34.970001,8844549,0,0
+2023-06-01 00:00:00+02:00,35.230000,35.380001,34.240002,35.349998,35.349998,6721030,0,0
+2023-05-31 00:00:00+02:00,3480,3548,3426,3501,3501,32605833,0,0
+2023-05-30 00:00:00+02:00,3439,3537,3385,3423,3423,8970804,0,0
+2023-05-29 00:00:00+02:00,3466,3506,3402,3432,3432,3912803,0,0
+2023-05-26 00:00:00+02:00,3475,3599,3433,3453,3453,6744718,0,0
+2023-05-25 00:00:00+02:00,3540,3609,3463,3507,3507,16900221,0,0
+2023-05-24 00:00:00+02:00,3620,3650,3526,3540,3540,9049505,0,0
+2023-05-23 00:00:00+02:00,3690,3667,3556,3610,3610,10797373,0,0
+2023-05-22 00:00:00+02:00,3705,3736,3609,3661,3661,7132641,0,0
+2023-05-19 00:00:00+02:00,3620,3715,3625,3690,3690,12648518,0,0
+2023-05-18 00:00:00+02:00,3657,3699,3584,3646,3646,10674542,0,0
+2023-05-17 00:00:00+02:00,3687,3731,3656,3671,3671,9892791,0,0
+2023-05-16 00:00:00+02:00,3715,3773,3696,3703,3703,4706789,0,0
+2023-05-15 00:00:00+02:00,3774,3805,3696,3727,3727,7890969,0,0
+2023-05-12 00:00:00+02:00,3750,3844,3671,3774,3774,8724303,0,0
+2023-05-11 00:00:00+02:00,3880,3888,3701,3732,3732,14371855,0,0
+2023-05-10 00:00:00+02:00,3893,3880,3642,3810,3810,30393389,0,0
+2023-05-09 00:00:00+02:00,4441,4441,3939,3966,3966,19833428,0,0
+2023-05-08 00:00:00+02:00,4463,4578,4456,4471,4471,11092519,0,0
+2023-05-05 00:00:00+02:00,4299,4490,4287,4458,4458,28539048,0,0
+2023-05-04 00:00:00+02:00,4149,4330,4123,4283,4283,15506868,0,0
+2023-05-03 00:00:00+02:00,3975,4098,3968,4095,4095,14657028,0,0
+2023-05-02 00:00:00+02:00,4037,4032,3917,3965,3965,11818133,0,0
--- a/tests/prices.py
+++ b/tests/prices.py
@@ -3,6 +3,7 @@ from .context import session_gbl

 import unittest

+import os
 import datetime as _dt
 import pytz as _tz
 import numpy as _np
@@ -58,7 +59,7 @@ class TestPriceHistory(unittest.TestCase):
            dt1 = df.index[-1]
            try:
                self.assertNotEqual(dt0.hour, dt1.hour)
-            except:
+            except AssertionError:
                print("Ticker = ", tkr)
                raise

@@ -81,7 +82,7 @@ class TestPriceHistory(unittest.TestCase):
            dt1 = df.index[-1]
            try:
                self.assertNotEqual(dt0, dt1)
-            except:
+            except AssertionError:
                print("Ticker = ", tkr)
                raise

@@ -105,7 +106,7 @@ class TestPriceHistory(unittest.TestCase):
            dt1 = df.index[-1]
            try:
                self.assertNotEqual(dt0.week, dt1.week)
-            except:
+            except AssertionError:
                print("Ticker={}: Last two rows within same week:".format(tkr))
                print(df.iloc[df.shape[0] - 2:])
                raise
@@ -114,6 +115,32 @@ class TestPriceHistory(unittest.TestCase):
            self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")

    def test_intraDayWithEvents(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present

        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
@@ -124,21 +151,46 @@ class TestPriceHistory(unittest.TestCase):
            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
            if df_daily_divs.shape[0] == 0:
-                # self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
                continue

            last_div_date = df_daily_divs.index[-1]
            start_d = last_div_date.date()
            end_d = last_div_date.date() + _dt.timedelta(days=1)
-            df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-            self.assertTrue((df["Dividends"] != 0.0).any())
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
            test_run = True
-            break

        if not test_run:
-            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")

    def test_dailyWithEvents(self):
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2023, 1, 1)
+
+        tkr_div_dates = {'BHP.AX': [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)],  # Yahoo claims 23-Feb but wrong because DST
+                         'IMP.JO': [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)],
+                         'BP.L': [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12),
+                                  _dt.date(2022, 2, 17)],
+                         'INTC': [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5),
+                                  _dt.date(2022, 2, 4)]}
+
+        for tkr, dates in tkr_div_dates.items():
+            df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
+            df_divs = df[df['Dividends'] != 0].sort_index(ascending=False)
+            try:
+                self.assertTrue((df_divs.index.date == dates).all())
+            except AssertionError:
+                print(f'- ticker = {tkr}')
+                print('- response:') ; print(df_divs.index.date)
+                print('- answer:') ; print(dates)
+                raise
+
+    def test_dailyWithEvents_bugs(self):
        # Reproduce issue #521
        tkr1 = "QQQ"
        tkr2 = "GDX"
@@ -150,7 +202,7 @@ class TestPriceHistory(unittest.TestCase):
        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
        try:
            self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
            missing_from_df1 = df2.index.difference(df1.index)
            missing_from_df2 = df1.index.difference(df2.index)
            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -165,13 +217,76 @@ class TestPriceHistory(unittest.TestCase):
            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
            try:
                self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                missing_from_df1 = df2.index.difference(df1.index)
                missing_from_df2 = df1.index.difference(df2.index)
                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
                print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
                raise

+        # Reproduce issue #1634 - 1d dividend out-of-range, should be prepended to prices
+        div_dt = _pd.Timestamp(2022, 7, 21).tz_localize("America/New_York")
+        df_dividends = _pd.DataFrame(data={"Dividends":[1.0]}, index=[div_dt])
+        df_prices = _pd.DataFrame(data={c:[1.0] for c in yf.const.price_colnames}|{'Volume':0}, index=[div_dt+_dt.timedelta(days=1)])
+        df_merged = yf.utils.safe_merge_dfs(df_prices, df_dividends, '1d')
+        self.assertEqual(df_merged.shape[0], 2)
+        self.assertTrue(df_merged[df_prices.columns].iloc[1:].equals(df_prices))
+        self.assertEqual(df_merged.index[0], div_dt)
+
+    def test_intraDayWithEvents(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
+        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
+
+        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
+        test_run = False
+        for tkr in tase_tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
+
    def test_weeklyWithEvents(self):
        # Reproduce issue #521
        tkr1 = "QQQ"
@@ -184,7 +299,7 @@ class TestPriceHistory(unittest.TestCase):
        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
        try:
            self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
            missing_from_df1 = df2.index.difference(df1.index)
            missing_from_df2 = df1.index.difference(df2.index)
            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -199,7 +314,7 @@ class TestPriceHistory(unittest.TestCase):
            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
            try:
                self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                missing_from_df1 = df2.index.difference(df1.index)
                missing_from_df2 = df1.index.difference(df2.index)
                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
@@ -217,7 +332,7 @@ class TestPriceHistory(unittest.TestCase):
        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
        try:
            self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
            missing_from_df1 = df2.index.difference(df1.index)
            missing_from_df2 = df1.index.difference(df2.index)
            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -232,7 +347,7 @@ class TestPriceHistory(unittest.TestCase):
            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
            try:
                self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                missing_from_df1 = df2.index.difference(df1.index)
                missing_from_df2 = df1.index.difference(df2.index)
                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
@@ -241,8 +356,19 @@ class TestPriceHistory(unittest.TestCase):

    def test_monthlyWithEvents2(self):
        # Simply check no exception from internal merge
-        tkr = "ABBV"
-        yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends'] != 0]
+        dfd_divs = dfd[dfd['Dividends'] != 0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
+
+        dfm = yf.Ticker("F").history(period="50mo", interval="1mo")
+        dfd = yf.Ticker("F").history(period="50mo", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends'] != 0]
+        dfd_divs = dfd[dfd['Dividends'] != 0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])

    def test_tz_dst_ambiguous(self):
        # Reproduce issue #1100
@@ -272,7 +398,7 @@ class TestPriceHistory(unittest.TestCase):
        df = dat.history(start=start, end=end, interval=interval)
        try:
            self.assertTrue((df.index.weekday == 0).all())
-        except:
+        except AssertionError:
            print("Weekly data not aligned to Monday")
            raise

@@ -324,18 +450,18 @@ class TestPriceHistory(unittest.TestCase):
        interval = "1h"
        interval_td = _dt.timedelta(hours=1)
        time_open = _dt.time(9)
-        time_close = _dt.time(17,30)
+        time_close = _dt.time(17, 30)
        special_day = _dt.date(2022, 12, 23)
        time_early_close = _dt.time(13, 2)
        dat = yf.Ticker(tkr, session=self.session)

        # Half trading day Jan 5, Apr 14, May 25, Jun 23, Nov 4, Dec 23, Dec 30
-        half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1,5), (4,14), (5,25), (6,23), (11,4), (12,23), (12,30)]]
+        half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1, 5), (4, 14), (5, 25), (6, 23), (11, 4), (12, 23), (12, 30)]]

        # Yahoo has incorrectly classified afternoon of 2022-04-13 as post-market.
        # Nothing yfinance can do because Yahoo doesn't return data with prepost=False.
        # But need to handle in this test.
-        expected_incorrect_half_days = [_dt.date(2022,4,13)]
+        expected_incorrect_half_days = [_dt.date(2022, 4, 13)]
        half_days = sorted(half_days+expected_incorrect_half_days)

        # Run
@@ -352,7 +478,7 @@ class TestPriceHistory(unittest.TestCase):
        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
        f_early_close = (last_dts+interval_td).dt.time < time_close
        early_close_dates = last_dts.index[f_early_close].values
-        unexpected_early_close_dates = [d for d in early_close_dates if not d in half_days]
+        unexpected_early_close_dates = [d for d in early_close_dates if d not in half_days]
        self.assertEqual(len(unexpected_early_close_dates), 0)
        self.assertEqual(len(early_close_dates), len(half_days))
        self.assertTrue(_np.equal(early_close_dates, half_days).all())
@@ -368,7 +494,7 @@ class TestPriceHistory(unittest.TestCase):
        interval = "1h"
        interval_td = _dt.timedelta(hours=1)
        time_open = _dt.time(10)
-        time_close = _dt.time(16,12)
+        time_close = _dt.time(16, 12)
        # No early closes in 2022
        dat = yf.Ticker(tkr, session=self.session)

@@ -405,6 +531,7 @@ class TestPriceHistory(unittest.TestCase):

        df = dat.history(start=start, end=end, interval=interval)

+
 class TestPriceRepair(unittest.TestCase):
    session = None

@@ -437,7 +564,7 @@ class TestPriceRepair(unittest.TestCase):
            start_dt = end_dt - td_60d
            df = dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)

-    def test_repair_100x_weekly(self):
+    def test_repair_100x_random_weekly(self):
        # Setup:
        tkr = "PNL.L"
        dat = yf.Ticker(tkr, session=self.session)
@@ -448,12 +575,12 @@ class TestPriceRepair(unittest.TestCase):
                                 "High":      [476,   476.5, 477,   480],
                                 "Low":       [470.5, 470,   465.5, 468.26],
                                 "Close":     [475,   473.5, 472,   473.5],
-                                 "Adj Close": [475,   473.5, 472,   473.5],
+                                 "Adj Close": [470.1, 468.6, 467.1, 468.6],
                                 "Volume": [2295613, 2245604, 3000287, 2635611]},
-                                index=_pd.to_datetime([_dt.date(2022, 10, 24),
-                                                       _dt.date(2022, 10, 17),
-                                                       _dt.date(2022, 10, 10),
-                                                       _dt.date(2022, 10, 3)]))
+                           index=_pd.to_datetime([_dt.date(2022, 10, 24),
+                                                  _dt.date(2022, 10, 17),
+                                                  _dt.date(2022, 10, 10),
+                                                  _dt.date(2022, 10, 3)]))
        df = df.sort_index()
        df.index.name = "Date"
        df_bad = df.copy()
@@ -465,18 +592,17 @@ class TestPriceRepair(unittest.TestCase):

        # Run test

-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)

        # First test - no errors left
        for c in data_cols:
            try:
                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                print(df[c])
                print(df_repaired[c])
                raise

-
        # Second test - all differences should be either ~1x or ~100x
        ratio = df_bad[data_cols].values / df[data_cols].values
        ratio = ratio.round(2)
@@ -491,7 +617,7 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

-    def test_repair_100x_weekly_preSplit(self):
+    def test_repair_100x_random_weekly_preSplit(self):
        # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.

        tkr = "PNL.L"
@@ -499,16 +625,16 @@ class TestPriceRepair(unittest.TestCase):
        tz_exchange = dat.fast_info["timezone"]

        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [400,   398,    392.5,   417],
-                                 "High":      [421,   425,    419,     420.5],
-                                 "Low":       [400,   380.5,  376.5,   396],
-                                 "Close":     [410,   409.5,  402,     399],
-                                 "Adj Close": [398.02, 397.53, 390.25, 387.34],
+        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
+                                 "High":      [421,    425,    419,    420.5],
+                                 "Low":       [400,    380.5,  376.5,  396],
+                                 "Close":     [410,    409.5,  402,    399],
+                                 "Adj Close": [393.91, 393.43, 386.22, 383.34],
                                 "Volume": [3232600, 3773900, 10835000, 4257900]},
-                                index=_pd.to_datetime([_dt.date(2020, 3, 30),
-                                                       _dt.date(2020, 3, 23),
-                                                       _dt.date(2020, 3, 16),
-                                                       _dt.date(2020, 3, 9)]))
+                           index=_pd.to_datetime([_dt.date(2020, 3, 30),
+                                                  _dt.date(2020, 3, 23),
+                                                  _dt.date(2020, 3, 16),
+                                                  _dt.date(2020, 3, 9)]))
        df = df.sort_index()
        # Simulate data missing split-adjustment:
        df[data_cols] *= 100.0
@@ -523,13 +649,13 @@ class TestPriceRepair(unittest.TestCase):
        df.index = df.index.tz_localize(tz_exchange)
        df_bad.index = df_bad.index.tz_localize(tz_exchange)

-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)

        # First test - no errors left
        for c in data_cols:
            try:
                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                print("Mismatch in column", c)
                print("- df_repaired:")
                print(df_repaired[c])
@@ -551,7 +677,7 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

-    def test_repair_100x_daily(self):
+    def test_repair_100x_random_daily(self):
        tkr = "PNL.L"
        dat = yf.Ticker(tkr, session=self.session)
        tz_exchange = dat.fast_info["timezone"]
@@ -563,10 +689,10 @@ class TestPriceRepair(unittest.TestCase):
                                 "Close":     [475.5,  475.5, 474.5, 475],
                                 "Adj Close": [475.5,  475.5, 474.5, 475],
                                 "Volume": [436414, 485947, 358067, 287620]},
-                            index=_pd.to_datetime([_dt.date(2022, 11, 1),
-                                                   _dt.date(2022, 10, 31),
-                                                   _dt.date(2022, 10, 28),
-                                                   _dt.date(2022, 10, 27)]))
+                           index=_pd.to_datetime([_dt.date(2022, 11, 1),
+                                                  _dt.date(2022, 10, 31),
+                                                  _dt.date(2022, 10, 28),
+                                                  _dt.date(2022, 10, 27)]))
        df = df.sort_index()
        df.index.name = "Date"
        df_bad = df.copy()
@@ -576,7 +702,7 @@ class TestPriceRepair(unittest.TestCase):
        df.index = df.index.tz_localize(tz_exchange)
        df_bad.index = df_bad.index.tz_localize(tz_exchange)

-        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)

        # First test - no errors left
        for c in data_cols:
@@ -596,6 +722,60 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

+    def test_repair_100x_block_daily(self):
+        # Some 100x errors are not sporadic.
+        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
+
+        tkrs = ['AET.L', 'SSW.JO']
+        for tkr in tkrs:
+            for interval in ['1d', '1wk']:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+                _dp = os.path.dirname(__file__)
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
+                if not os.path.isfile(fp):
+                    continue
+                df_bad = _pd.read_csv(fp, index_col="Date")
+                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
+                df_bad = df_bad.sort_index()
+
+                df = df_bad.copy()
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
+                df = _pd.read_csv(fp, index_col="Date")
+                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
+                df = df.sort_index()
+
+                df_repaired = dat._fix_unit_switch(df_bad, interval, tz_exchange)
+                df_repaired = df_repaired.sort_index()
+
+                # First test - no errors left
+                for c in data_cols:
+                    try:
+                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+                    except:
+                        print("- repaired:")
+                        print(df_repaired[c])
+                        print("- correct:")
+                        print(df[c])
+                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
+                        raise
+
+                # Second test - all differences should be either ~1x or ~100x
+                ratio = df_bad[data_cols].values / df[data_cols].values
+                ratio = ratio.round(2)
+                # - round near-100 ratio to 100:
+                f = ratio > 90
+                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+                # - now test
+                f_100 = (ratio == 100) | (ratio == 0.01)
+                f_1 = ratio == 1
+                self.assertTrue((f_100 | f_1).all())
+
+                self.assertTrue("Repaired?" in df_repaired.columns)
+                self.assertFalse(df_repaired["Repaired?"].isna().any())
+
    def test_repair_zeroes_daily(self):
        tkr = "BBIL.L"
        dat = yf.Ticker(tkr, session=self.session)
@@ -607,9 +787,9 @@ class TestPriceRepair(unittest.TestCase):
                                     "Close":     [103.03, 102.05, 102.08],
                                     "Adj Close": [102.03, 102.05, 102.08],
                                     "Volume": [560, 137, 117]},
-                                index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
-                                                       _dt.datetime(2022, 10, 31),
-                                                       _dt.datetime(2022, 10, 30)]))
+                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
+                                                      _dt.datetime(2022, 10, 31),
+                                                      _dt.datetime(2022, 10, 30)]))
        df_bad = df_bad.sort_index()
        df_bad.index.name = "Date"
        df_bad.index = df_bad.index.tz_localize(tz_exchange)
@@ -626,6 +806,42 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in repaired_df.columns)
        self.assertFalse(repaired_df["Repaired?"].isna().any())

+    def test_repair_zeroes_daily_adjClose(self):
+        # Test that 'Adj Close' is reconstructed correctly, 
+        # particularly when a dividend occurred within 1 day.
+
+        tkr = "INTC"
+        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
+                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
+                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
+                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
+                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
+                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
+                                 "Dividends": [0, 0, 0.365, 0, 0]},
+                           index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
+                                                  _dt.datetime(2023, 2, 7),
+                                                  _dt.datetime(2023, 2, 6),
+                                                  _dt.datetime(2023, 2, 3),
+                                                  _dt.datetime(2023, 2, 2)]))
+        df = df.sort_index()
+        df.index.name = "Date"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        df.index = df.index.tz_localize(tz_exchange)
+
+        rtol = 5e-3
+        for i in [0, 1, 2]:
+            df_slice = df.iloc[i:i+3]
+            for j in range(3):
+                df_slice_bad = df_slice.copy()
+                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
+
+                df_slice_bad_repaired = dat._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
+                for c in ["Close", "Adj Close"]:
+                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
+                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
+                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
+
    def test_repair_zeroes_hourly(self):
        tkr = "INTC"
        dat = yf.Ticker(tkr, session=self.session)
@@ -647,7 +863,7 @@ class TestPriceRepair(unittest.TestCase):
        for c in ["Open", "Low", "High", "Close"]:
            try:
                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
-            except:
+            except AssertionError:
                print("COLUMN", c)
                print("- repaired_df")
                print(repaired_df)
@@ -660,5 +876,130 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in repaired_df.columns)
        self.assertFalse(repaired_df["Repaired?"].isna().any())

+    def test_repair_bad_stock_split(self):
+        # Stocks that split in 2022 but no problems in Yahoo data, 
+        # so repair should change nothing
+        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
+        good_tkrs += ['AEI', 'CHRA', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
+        good_tkrs += ['BOL.ST', 'TUI1.DE']
+        intervals = ['1d', '1wk', '1mo', '3mo']
+        for tkr in good_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
+
+                repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
+        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz_exchange = dat.fast_info["timezone"]
+
+            _dp = os.path.dirname(__file__)
+            interval = '1d'
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            if not os.path.isfile(fp):
+                interval = '1wk'
+                fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            df_bad = _pd.read_csv(fp, index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+
+            repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
+
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
+            correct_df = _pd.read_csv(fp, index_col="Date")
+            correct_df.index = _pd.to_datetime(correct_df.index)
+
+            repaired_df = repaired_df.sort_index()
+            correct_df = correct_df.sort_index()
+            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                try:
+                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+                except AssertionError:
+                    print(f"tkr={tkr} COLUMN={c}")
+                    # print("- repaired_df")
+                    # print(repaired_df)
+                    # print("- correct_df[c]:")
+                    # print(correct_df[c])
+                    # print("- diff:")
+                    # print(repaired_df[c] - correct_df[c])
+                    raise
+
+        # Had very high price volatility in Jan-2021 around split date that could 
+        # be mistaken for missing stock split adjustment. And old logic did think 
+        # column 'High' required fixing - wrong!
+        sketchy_tkrs = ['FIZZ']
+        intervals = ['1wk']
+        for tkr in sketchy_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
+
+                repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except AssertionError:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+    def test_repair_missing_div_adjust(self):
+        tkr = '8TRA.DE'
+
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        _dp = os.path.dirname(__file__)
+        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
+        df_bad.index = _pd.to_datetime(df_bad.index)
+
+        repaired_df = dat._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
+
+        correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
+        correct_df.index = _pd.to_datetime(correct_df.index)
+
+        repaired_df = repaired_df.sort_index()
+        correct_df = correct_df.sort_index()
+        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+            try:
+                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+            except:
+                print(f"tkr={tkr} COLUMN={c}")
+                print("- repaired_df")
+                print(repaired_df)
+                print("- correct_df[c]:")
+                print(correct_df[c])
+                print("- diff:")
+                print(repaired_df[c] - correct_df[c])
+                raise
+
+
 if __name__ == '__main__':
    unittest.main()
--- a/tests/ticker.py
+++ b/tests/ticker.py
@@ -25,6 +25,8 @@ class TestTicker(unittest.TestCase):
    def setUpClass(cls):
        cls.session = session_gbl

+        cls.proxy = None
+
    @classmethod
    def tearDownClass(cls):
        if cls.session is not None:
@@ -71,19 +73,20 @@ class TestTicker(unittest.TestCase):
        dat.news
        dat.earnings_dates

-        # These require decryption which is broken:
+        dat.income_stmt
+        dat.quarterly_income_stmt
+        dat.balance_sheet
+        dat.quarterly_balance_sheet
+        dat.cashflow
+        dat.quarterly_cashflow
+
+        # These haven't been ported Yahoo API
        # dat.shares
        # dat.info
        # dat.calendar
        # dat.recommendations
        # dat.earnings
        # dat.quarterly_earnings
-        # dat.income_stmt
-        # dat.quarterly_income_stmt
-        # dat.balance_sheet
-        # dat.quarterly_balance_sheet
-        # dat.cashflow
-        # dat.quarterly_cashflow
        # dat.recommendations_summary
        # dat.analyst_price_target
        # dat.revenue_forecasts
@@ -122,6 +125,13 @@ class TestTicker(unittest.TestCase):
            dat.news
            dat.earnings_dates

+            dat.income_stmt
+            dat.quarterly_income_stmt
+            dat.balance_sheet
+            dat.quarterly_balance_sheet
+            dat.cashflow
+            dat.quarterly_cashflow
+
            # These require decryption which is broken:
            # dat.shares
            # dat.info
@@ -129,12 +139,6 @@ class TestTicker(unittest.TestCase):
            # dat.recommendations
            # dat.earnings
            # dat.quarterly_earnings
-            # dat.income_stmt
-            # dat.quarterly_income_stmt
-            # dat.balance_sheet
-            # dat.quarterly_balance_sheet
-            # dat.cashflow
-            # dat.quarterly_cashflow
            # dat.recommendations_summary
            # dat.analyst_price_target
            # dat.revenue_forecasts
@@ -142,6 +146,129 @@ class TestTicker(unittest.TestCase):
            # dat.earnings_trend
            # dat.earnings_forecasts

+    def test_goodTicker_withProxy(self):
+        # that yfinance works when full api is called on same instance of ticker
+
+        tkr = "IBM"
+        dat = yf.Ticker(tkr, session=self.session)
+
+        dat._fetch_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat._get_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat.history(period="1wk", proxy=self.proxy)
+
+        v = dat.stats(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_recommendations(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_calendar(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_major_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_institutional_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_mutualfund_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_info(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_sustainability(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_recommendations_summary(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_analyst_price_target(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_rev_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_trend_details(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_trend(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_income_stmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_incomestmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_financials(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balance_sheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balancesheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cash_flow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cashflow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares_full(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_isin(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(v != "")
+
+        v = dat.get_news(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_earnings_dates(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        # TODO: enable after merge
+        # dat.get_history_metadata(proxy=self.proxy)
+        # self.assertIsNotNone(v)
+        # self.assertTrue(len(v) > 0)
+

 class TestTickerHistory(unittest.TestCase):
    session = None
@@ -211,7 +338,7 @@ class TestTickerHistory(unittest.TestCase):
        self.assertFalse(data.empty, "data is empty")


-# Below will fail because decryption broken
+# Below will fail because not ported to Yahoo API
 # class TestTickerEarnings(unittest.TestCase):
 #     session = None

@@ -367,270 +494,243 @@ class TestTickerMiscFinancials(unittest.TestCase):
        self.assertIsInstance(data, pd.Series, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-    # Below will fail because decryption broken
+    def test_income_statement(self):
+        expected_keys = ["Total Revenue", "Basic EPS"]
+        expected_periods_days = 365

-    # def test_income_statement(self):
-    #     expected_keys = ["Total Revenue", "Basic EPS"]
-    #     expected_periods_days = 365
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")

-    #     # Test contents of table
-    #     data = self.ticker.get_income_stmt(pretty=True)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+        # Test property defaults
+        data2 = self.ticker.income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    #     # Test property defaults
-    #     data2 = self.ticker.income_stmt
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_income_stmt(pretty=False)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        # Test to_dict
+        data = self.ticker.get_income_stmt(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")

-    #     # Test to_dict
-    #     data = self.ticker.get_income_stmt(as_dict=True)
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+    def test_quarterly_income_statement(self):
+        expected_keys = ["Total Revenue", "Basic EPS"]
+        expected_periods_days = 365//4

-    # def test_quarterly_income_statement(self):
-    #     expected_keys = ["Total Revenue", "Basic EPS"]
-    #     expected_periods_days = 365//4
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")

-    #     # Test contents of table
-    #     data = self.ticker.get_income_stmt(pretty=True, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+        # Test property defaults
+        data2 = self.ticker.quarterly_income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    #     # Test property defaults
-    #     data2 = self.ticker.quarterly_income_stmt
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_income_stmt(pretty=False, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        # Test to_dict
+        data = self.ticker.get_income_stmt(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")

-    #     # Test to_dict
-    #     data = self.ticker.get_income_stmt(as_dict=True)
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+    def test_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365

-    # def test_quarterly_income_statement_old_fmt(self):
-    #     expected_row = "TotalRevenue"
-    #     data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")

-    #     data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-    #     self.assertIs(data, data_cached, "data not cached")
+        # Test property defaults
+        data2 = self.ticker.balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    # def test_balance_sheet(self):
-    #     expected_keys = ["Total Assets", "Net PPE"]
-    #     expected_periods_days = 365
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     # Test contents of table
-    #     data = self.ticker.get_balance_sheet(pretty=True)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")

-    #     # Test property defaults
-    #     data2 = self.ticker.balance_sheet
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+    def test_quarterly_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365//4

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_balance_sheet(pretty=False)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")

-    #     # Test to_dict
-    #     data = self.ticker.get_balance_sheet(as_dict=True)
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+        # Test property defaults
+        data2 = self.ticker.quarterly_balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    # def test_quarterly_balance_sheet(self):
-    #     expected_keys = ["Total Assets", "Net PPE"]
-    #     expected_periods_days = 365//4
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     # Test contents of table
-    #     data = self.ticker.get_balance_sheet(pretty=True, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
+        self.assertIsInstance(data, dict, "data has wrong type")

-    #     # Test property defaults
-    #     data2 = self.ticker.quarterly_balance_sheet
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+    def test_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_balance_sheet(pretty=False, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")

-    #     # Test to_dict
-    #     data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+        # Test property defaults
+        data2 = self.ticker.cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    # def test_quarterly_balance_sheet_old_fmt(self):
-    #     expected_row = "TotalAssets"
-    #     data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-    #     self.assertIs(data, data_cached, "data not cached")
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")

-    # def test_cash_flow(self):
-    #     expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
-    #     expected_periods_days = 365
+    def test_quarterly_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365//4

-    #     # Test contents of table
-    #     data = self.ticker.get_cashflow(pretty=True)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")

-    #     # Test property defaults
-    #     data2 = self.ticker.cashflow
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+        # Test property defaults
+        data2 = self.ticker.quarterly_cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_cashflow(pretty=False)
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")

-    #     # Test to_dict
-    #     data = self.ticker.get_cashflow(as_dict=True)
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")

-    # def test_quarterly_cash_flow(self):
-    #     expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
-    #     expected_periods_days = 365//4
+    def test_income_alt_names(self):
+        i1 = self.ticker.income_stmt
+        i2 = self.ticker.incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.financials
+        self.assertTrue(i1.equals(i3))

-    #     # Test contents of table
-    #     data = self.ticker.get_cashflow(pretty=True, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
-    #     period = abs((data.columns[0]-data.columns[1]).days)
-    #     self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+        i1 = self.ticker.get_income_stmt()
+        i2 = self.ticker.get_incomestmt()
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials()
+        self.assertTrue(i1.equals(i3))

-    #     # Test property defaults
-    #     data2 = self.ticker.quarterly_cashflow
-    #     self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+        i1 = self.ticker.quarterly_income_stmt
+        i2 = self.ticker.quarterly_incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.quarterly_financials
+        self.assertTrue(i1.equals(i3))

-    #     # Test pretty=False
-    #     expected_keys = [k.replace(' ', '') for k in expected_keys]
-    #     data = self.ticker.get_cashflow(pretty=False, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     for k in expected_keys:
-    #         self.assertIn(k, data.index, "Did not find expected row in index")
+        i1 = self.ticker.get_income_stmt(freq="quarterly")
+        i2 = self.ticker.get_incomestmt(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials(freq="quarterly")
+        self.assertTrue(i1.equals(i3))

-    #     # Test to_dict
-    #     data = self.ticker.get_cashflow(as_dict=True)
-    #     self.assertIsInstance(data, dict, "data has wrong type")
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))

-    # def test_quarterly_cashflow_old_fmt(self):
-    #     expected_row = "NetIncome"
-    #     data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))

-    #     data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-    #     self.assertIs(data, data_cached, "data not cached")
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))

-    # def test_income_alt_names(self):
-    #     i1 = self.ticker.income_stmt
-    #     i2 = self.ticker.incomestmt
-    #     self.assertTrue(i1.equals(i2))
-    #     i3 = self.ticker.financials
-    #     self.assertTrue(i1.equals(i3))
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))

-    #     i1 = self.ticker.get_income_stmt()
-    #     i2 = self.ticker.get_incomestmt()
-    #     self.assertTrue(i1.equals(i2))
-    #     i3 = self.ticker.get_financials()
-    #     self.assertTrue(i1.equals(i3))
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))

-    #     i1 = self.ticker.quarterly_income_stmt
-    #     i2 = self.ticker.quarterly_incomestmt
-    #     self.assertTrue(i1.equals(i2))
-    #     i3 = self.ticker.quarterly_financials
-    #     self.assertTrue(i1.equals(i3))
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))

-    #     i1 = self.ticker.get_income_stmt(freq="quarterly")
-    #     i2 = self.ticker.get_incomestmt(freq="quarterly")
-    #     self.assertTrue(i1.equals(i2))
-    #     i3 = self.ticker.get_financials(freq="quarterly")
-    #     self.assertTrue(i1.equals(i3))
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))

-    # def test_balance_sheet_alt_names(self):
-    #     i1 = self.ticker.balance_sheet
-    #     i2 = self.ticker.balancesheet
-    #     self.assertTrue(i1.equals(i2))
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))

-    #     i1 = self.ticker.get_balance_sheet()
-    #     i2 = self.ticker.get_balancesheet()
-    #     self.assertTrue(i1.equals(i2))
+    def test_bad_freq_value_raises_exception(self):
+        self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))

-    #     i1 = self.ticker.quarterly_balance_sheet
-    #     i2 = self.ticker.quarterly_balancesheet
-    #     self.assertTrue(i1.equals(i2))
-
-    #     i1 = self.ticker.get_balance_sheet(freq="quarterly")
-    #     i2 = self.ticker.get_balancesheet(freq="quarterly")
-    #     self.assertTrue(i1.equals(i2))
-
-    # def test_cash_flow_alt_names(self):
-    #     i1 = self.ticker.cash_flow
-    #     i2 = self.ticker.cashflow
-    #     self.assertTrue(i1.equals(i2))
-
-    #     i1 = self.ticker.get_cash_flow()
-    #     i2 = self.ticker.get_cashflow()
-    #     self.assertTrue(i1.equals(i2))
-
-    #     i1 = self.ticker.quarterly_cash_flow
-    #     i2 = self.ticker.quarterly_cashflow
-    #     self.assertTrue(i1.equals(i2))
-
-    #     i1 = self.ticker.get_cash_flow(freq="quarterly")
-    #     i2 = self.ticker.get_cashflow(freq="quarterly")
-    #     self.assertTrue(i1.equals(i2))
+    # Below will fail because not ported to Yahoo API

    # def test_sustainability(self):
    #     data = self.ticker.sustainability
@@ -685,9 +785,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
    #     self.assertFalse(data.empty, "data is empty")

-    # def test_bad_freq_value_raises_exception(self):
-    #     self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
-

 class TestTickerInfo(unittest.TestCase):
    session = None
@@ -717,17 +814,16 @@ class TestTickerInfo(unittest.TestCase):
        for k in f:
            self.assertIsNotNone(f[k])

-    # Below will fail because decryption broken
-
-    # def test_info(self):
-    #     data = self.tickers[0].info
-    #     self.assertIsInstance(data, dict, "data has wrong type")
-    #     self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
-    #     self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
+    def test_info(self):
+        data = self.tickers[0].info
+        self.assertIsInstance(data, dict, "data has wrong type")
+        expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
+        for k in expected_keys:
+            print(k)
+            self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' in info dict")
+        self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")

    # def test_fast_info_matches_info(self):
-    #     yf.scrapers.quote.PRUNE_INFO = False
-
    #     fast_info_keys = set()
    #     for ticker in self.tickers:
    #         fast_info_keys.update(set(ticker.fast_info.keys()))
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -0,0 +1,51 @@
+"""
+Tests for utils
+
+To run all tests in suite from commandline:
+   python -m unittest tests.utils
+
+Specific test class:
+   python -m unittest tests.utils.TestTicker
+
+"""
+# import pandas as pd
+# import numpy as np
+
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+# import requests_cache
+import tempfile
+
+
+class TestUtils(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tempCacheDir = tempfile.TemporaryDirectory()
+        yf.set_tz_cache_location(cls.tempCacheDir.name)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.tempCacheDir.cleanup()
+
+    def test_storeTzNoRaise(self):
+        # storing TZ to cache should never raise exception
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        tz2 = "London/Europe"
+        cache = yf.utils.get_tz_cache()
+        cache.store(tkr, tz1)
+        cache.store(tkr, tz2)
+
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(TestUtils('Test utils'))
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()
--- a/yfinance/init.py
+++ b/yfinance/init.py
@@ -23,7 +23,7 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
-from .utils import set_tz_cache_location
+from .utils import set_tz_cache_location, enable_debug_mode

 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -43,4 +43,4 @@ def pdr_override():
        pass


-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']
+__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'enable_debug_mode', 'set_tz_cache_location']
--- a/yfinance/base.py
+++ b/yfinance/base.py
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -0,0 +1,118 @@
+_BASE_URL_ = 'https://query2.finance.yahoo.com'
+_ROOT_URL_ = 'https://finance.yahoo.com'
+
+fundamentals_keys = {
+    'financials': ["TaxEffectOfUnusualItems", "TaxRateForCalcs", "NormalizedEBITDA", "NormalizedDilutedEPS",
+                   "NormalizedBasicEPS", "TotalUnusualItems", "TotalUnusualItemsExcludingGoodwill",
+                   "NetIncomeFromContinuingOperationNetMinorityInterest", "ReconciledDepreciation",
+                   "ReconciledCostOfRevenue", "EBITDA", "EBIT", "NetInterestIncome", "InterestExpense",
+                   "InterestIncome", "ContinuingAndDiscontinuedDilutedEPS", "ContinuingAndDiscontinuedBasicEPS",
+                   "NormalizedIncome", "NetIncomeFromContinuingAndDiscontinuedOperation", "TotalExpenses",
+                   "RentExpenseSupplemental", "ReportedNormalizedDilutedEPS", "ReportedNormalizedBasicEPS",
+                   "TotalOperatingIncomeAsReported", "DividendPerShare", "DilutedAverageShares", "BasicAverageShares",
+                   "DilutedEPS", "DilutedEPSOtherGainsLosses", "TaxLossCarryforwardDilutedEPS",
+                   "DilutedAccountingChange", "DilutedExtraordinary", "DilutedDiscontinuousOperations",
+                   "DilutedContinuousOperations", "BasicEPS", "BasicEPSOtherGainsLosses", "TaxLossCarryforwardBasicEPS",
+                   "BasicAccountingChange", "BasicExtraordinary", "BasicDiscontinuousOperations",
+                   "BasicContinuousOperations", "DilutedNIAvailtoComStockholders", "AverageDilutionEarnings",
+                   "NetIncomeCommonStockholders", "OtherunderPreferredStockDividend", "PreferredStockDividends",
+                   "NetIncome", "MinorityInterests", "NetIncomeIncludingNoncontrollingInterests",
+                   "NetIncomeFromTaxLossCarryforward", "NetIncomeExtraordinary", "NetIncomeDiscontinuousOperations",
+                   "NetIncomeContinuousOperations", "EarningsFromEquityInterestNetOfTax", "TaxProvision",
+                   "PretaxIncome", "OtherIncomeExpense", "OtherNonOperatingIncomeExpenses", "SpecialIncomeCharges",
+                   "GainOnSaleOfPPE", "GainOnSaleOfBusiness", "OtherSpecialCharges", "WriteOff",
+                   "ImpairmentOfCapitalAssets", "RestructuringAndMergernAcquisition", "SecuritiesAmortization",
+                   "EarningsFromEquityInterest", "GainOnSaleOfSecurity", "NetNonOperatingInterestIncomeExpense",
+                   "TotalOtherFinanceCost", "InterestExpenseNonOperating", "InterestIncomeNonOperating",
+                   "OperatingIncome", "OperatingExpense", "OtherOperatingExpenses", "OtherTaxes",
+                   "ProvisionForDoubtfulAccounts", "DepreciationAmortizationDepletionIncomeStatement",
+                   "DepletionIncomeStatement", "DepreciationAndAmortizationInIncomeStatement", "Amortization",
+                   "AmortizationOfIntangiblesIncomeStatement", "DepreciationIncomeStatement", "ResearchAndDevelopment",
+                   "SellingGeneralAndAdministration", "SellingAndMarketingExpense", "GeneralAndAdministrativeExpense",
+                   "OtherGandA", "InsuranceAndClaims", "RentAndLandingFees", "SalariesAndWages", "GrossProfit",
+                   "CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue"],
+    'balance-sheet': ["TreasurySharesNumber", "PreferredSharesNumber", "OrdinarySharesNumber", "ShareIssued", "NetDebt",
+                      "TotalDebt", "TangibleBookValue", "InvestedCapital", "WorkingCapital", "NetTangibleAssets",
+                      "CapitalLeaseObligations", "CommonStockEquity", "PreferredStockEquity", "TotalCapitalization",
+                      "TotalEquityGrossMinorityInterest", "MinorityInterest", "StockholdersEquity",
+                      "OtherEquityInterest", "GainsLossesNotAffectingRetainedEarnings", "OtherEquityAdjustments",
+                      "FixedAssetsRevaluationReserve", "ForeignCurrencyTranslationAdjustments",
+                      "MinimumPensionLiabilities", "UnrealizedGainLoss", "TreasuryStock", "RetainedEarnings",
+                      "AdditionalPaidInCapital", "CapitalStock", "OtherCapitalStock", "CommonStock", "PreferredStock",
+                      "TotalPartnershipCapital", "GeneralPartnershipCapital", "LimitedPartnershipCapital",
+                      "TotalLiabilitiesNetMinorityInterest", "TotalNonCurrentLiabilitiesNetMinorityInterest",
+                      "OtherNonCurrentLiabilities", "LiabilitiesHeldforSaleNonCurrent", "RestrictedCommonStock",
+                      "PreferredSecuritiesOutsideStockEquity", "DerivativeProductLiabilities", "EmployeeBenefits",
+                      "NonCurrentPensionAndOtherPostretirementBenefitPlans", "NonCurrentAccruedExpenses",
+                      "DuetoRelatedPartiesNonCurrent", "TradeandOtherPayablesNonCurrent",
+                      "NonCurrentDeferredLiabilities", "NonCurrentDeferredRevenue",
+                      "NonCurrentDeferredTaxesLiabilities", "LongTermDebtAndCapitalLeaseObligation",
+                      "LongTermCapitalLeaseObligation", "LongTermDebt", "LongTermProvisions", "CurrentLiabilities",
+                      "OtherCurrentLiabilities", "CurrentDeferredLiabilities", "CurrentDeferredRevenue",
+                      "CurrentDeferredTaxesLiabilities", "CurrentDebtAndCapitalLeaseObligation",
+                      "CurrentCapitalLeaseObligation", "CurrentDebt", "OtherCurrentBorrowings", "LineOfCredit",
+                      "CommercialPaper", "CurrentNotesPayable", "PensionandOtherPostRetirementBenefitPlansCurrent",
+                      "CurrentProvisions", "PayablesAndAccruedExpenses", "CurrentAccruedExpenses", "InterestPayable",
+                      "Payables", "OtherPayable", "DuetoRelatedPartiesCurrent", "DividendsPayable", "TotalTaxPayable",
+                      "IncomeTaxPayable", "AccountsPayable", "TotalAssets", "TotalNonCurrentAssets",
+                      "OtherNonCurrentAssets", "DefinedPensionBenefit", "NonCurrentPrepaidAssets",
+                      "NonCurrentDeferredAssets", "NonCurrentDeferredTaxesAssets", "DuefromRelatedPartiesNonCurrent",
+                      "NonCurrentNoteReceivables", "NonCurrentAccountsReceivable", "FinancialAssets",
+                      "InvestmentsAndAdvances", "OtherInvestments", "InvestmentinFinancialAssets",
+                      "HeldToMaturitySecurities", "AvailableForSaleSecurities",
+                      "FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal", "TradingSecurities",
+                      "LongTermEquityInvestment", "InvestmentsinJointVenturesatCost",
+                      "InvestmentsInOtherVenturesUnderEquityMethod", "InvestmentsinAssociatesatCost",
+                      "InvestmentsinSubsidiariesatCost", "InvestmentProperties", "GoodwillAndOtherIntangibleAssets",
+                      "OtherIntangibleAssets", "Goodwill", "NetPPE", "AccumulatedDepreciation", "GrossPPE", "Leases",
+                      "ConstructionInProgress", "OtherProperties", "MachineryFurnitureEquipment",
+                      "BuildingsAndImprovements", "LandAndImprovements", "Properties", "CurrentAssets",
+                      "OtherCurrentAssets", "HedgingAssetsCurrent", "AssetsHeldForSaleCurrent", "CurrentDeferredAssets",
+                      "CurrentDeferredTaxesAssets", "RestrictedCash", "PrepaidAssets", "Inventory",
+                      "InventoriesAdjustmentsAllowances", "OtherInventories", "FinishedGoods", "WorkInProcess",
+                      "RawMaterials", "Receivables", "ReceivablesAdjustmentsAllowances", "OtherReceivables",
+                      "DuefromRelatedPartiesCurrent", "TaxesReceivable", "AccruedInterestReceivable", "NotesReceivable",
+                      "LoansReceivable", "AccountsReceivable", "AllowanceForDoubtfulAccountsReceivable",
+                      "GrossAccountsReceivable", "CashCashEquivalentsAndShortTermInvestments",
+                      "OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial"],
+    'cash-flow': ["ForeignSales", "DomesticSales", "AdjustedGeographySegmentData", "FreeCashFlow",
+                  "RepurchaseOfCapitalStock", "RepaymentOfDebt", "IssuanceOfDebt", "IssuanceOfCapitalStock",
+                  "CapitalExpenditure", "InterestPaidSupplementalData", "IncomeTaxPaidSupplementalData",
+                  "EndCashPosition", "OtherCashAdjustmentOutsideChangeinCash", "BeginningCashPosition",
+                  "EffectOfExchangeRateChanges", "ChangesInCash", "OtherCashAdjustmentInsideChangeinCash",
+                  "CashFlowFromDiscontinuedOperation", "FinancingCashFlow", "CashFromDiscontinuedFinancingActivities",
+                  "CashFlowFromContinuingFinancingActivities", "NetOtherFinancingCharges", "InterestPaidCFF",
+                  "ProceedsFromStockOptionExercised", "CashDividendsPaid", "PreferredStockDividendPaid",
+                  "CommonStockDividendPaid", "NetPreferredStockIssuance", "PreferredStockPayments",
+                  "PreferredStockIssuance", "NetCommonStockIssuance", "CommonStockPayments", "CommonStockIssuance",
+                  "NetIssuancePaymentsOfDebt", "NetShortTermDebtIssuance", "ShortTermDebtPayments",
+                  "ShortTermDebtIssuance", "NetLongTermDebtIssuance", "LongTermDebtPayments", "LongTermDebtIssuance",
+                  "InvestingCashFlow", "CashFromDiscontinuedInvestingActivities",
+                  "CashFlowFromContinuingInvestingActivities", "NetOtherInvestingChanges", "InterestReceivedCFI",
+                  "DividendsReceivedCFI", "NetInvestmentPurchaseAndSale", "SaleOfInvestment", "PurchaseOfInvestment",
+                  "NetInvestmentPropertiesPurchaseAndSale", "SaleOfInvestmentProperties",
+                  "PurchaseOfInvestmentProperties", "NetBusinessPurchaseAndSale", "SaleOfBusiness",
+                  "PurchaseOfBusiness", "NetIntangiblesPurchaseAndSale", "SaleOfIntangibles", "PurchaseOfIntangibles",
+                  "NetPPEPurchaseAndSale", "SaleOfPPE", "PurchaseOfPPE", "CapitalExpenditureReported",
+                  "OperatingCashFlow", "CashFromDiscontinuedOperatingActivities",
+                  "CashFlowFromContinuingOperatingActivities", "TaxesRefundPaid", "InterestReceivedCFO",
+                  "InterestPaidCFO", "DividendReceivedCFO", "DividendPaidCFO", "ChangeInWorkingCapital",
+                  "ChangeInOtherWorkingCapital", "ChangeInOtherCurrentLiabilities", "ChangeInOtherCurrentAssets",
+                  "ChangeInPayablesAndAccruedExpense", "ChangeInAccruedExpense", "ChangeInInterestPayable",
+                  "ChangeInPayable", "ChangeInDividendPayable", "ChangeInAccountPayable", "ChangeInTaxPayable",
+                  "ChangeInIncomeTaxPayable", "ChangeInPrepaidAssets", "ChangeInInventory", "ChangeInReceivables",
+                  "ChangesInAccountReceivables", "OtherNonCashItems", "ExcessTaxBenefitFromStockBasedCompensation",
+                  "StockBasedCompensation", "UnrealizedGainLossOnInvestmentSecurities", "ProvisionandWriteOffofAssets",
+                  "AssetImpairmentCharge", "AmortizationOfSecurities", "DeferredTax", "DeferredIncomeTax",
+                  "DepreciationAmortizationDepletion", "Depletion", "DepreciationAndAmortization",
+                  "AmortizationCashFlow", "AmortizationOfIntangibles", "Depreciation", "OperatingGainsLosses",
+                  "PensionAndEmployeeBenefitExpense", "EarningsLossesFromEquityInvestments",
+                  "GainLossOnInvestmentSecurities", "NetForeignCurrencyExchangeGainLoss", "GainLossOnSaleOfPPE",
+                  "GainLossOnSaleOfBusiness", "NetIncomeFromContinuingOperations",
+                  "CashFlowsfromusedinOperatingActivitiesDirect", "TaxesRefundPaidDirect", "InterestReceivedDirect",
+                  "InterestPaidDirect", "DividendsReceivedDirect", "DividendsPaidDirect", "ClassesofCashPayments",
+                  "OtherCashPaymentsfromOperatingActivities", "PaymentsonBehalfofEmployees",
+                  "PaymentstoSuppliersforGoodsandServices", "ClassesofCashReceiptsfromOperatingActivities",
+                  "OtherCashReceiptsfromOperatingActivities", "ReceiptsfromGovernmentGrants", "ReceiptsfromCustomers"]}
+
+price_colnames = ['Open', 'High', 'Low', 'Close', 'Adj Close']
--- a/yfinance/data.py
+++ b/yfinance/data.py
@@ -2,36 +2,18 @@ import functools
 from functools import lru_cache

 import logging
-import hashlib
-from base64 import b64decode
-usePycryptodome = False  # slightly faster
-# usePycryptodome = True
-if usePycryptodome:
-    from Crypto.Cipher import AES
-    from Crypto.Util.Padding import unpad
-else:
-    from cryptography.hazmat.primitives import padding
-    from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes

 import requests as requests
 import re
-from bs4 import BeautifulSoup
 import random
 import time

 from frozendict import frozendict

-try:
-    import ujson as json
-except ImportError:
-    import json as json
-
 from . import utils

 cache_maxsize = 64

-logger = utils.get_yf_logger()
-

 def lru_cache_freezeargs(func):
    """
@@ -54,127 +36,6 @@ def lru_cache_freezeargs(func):
    return wrapped


-def _extract_extra_keys_from_stores(data):
-    new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
-    new_keys_values = set([data[k] for k in new_keys])
-
-    # Maybe multiple keys have same value - keep one of each
-    new_keys_uniq = []
-    new_keys_uniq_values = set()
-    for k in new_keys:
-        v = data[k]
-        if not v in new_keys_uniq_values:
-            new_keys_uniq.append(k)
-            new_keys_uniq_values.add(v)
-
-    return [data[k] for k in new_keys_uniq]
-
-
-def decrypt_cryptojs_aes_stores(data, keys=None):
-    encrypted_stores = data['context']['dispatcher']['stores']
-
-    password = None
-    if keys is not None:
-        if not isinstance(keys, list):
-            raise TypeError("'keys' must be list")
-        candidate_passwords = keys
-    else:
-        candidate_passwords = []
-
-    if "_cs" in data and "_cr" in data:
-        _cs = data["_cs"]
-        _cr = data["_cr"]
-        _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
-        password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
-
-    encrypted_stores = b64decode(encrypted_stores)
-    assert encrypted_stores[0:8] == b"Salted__"
-    salt = encrypted_stores[8:16]
-    encrypted_stores = encrypted_stores[16:]
-
-    def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
-        """OpenSSL EVP Key Derivation Function
-        Args:
-            password (Union[str, bytes, bytearray]): Password to generate key from.
-            salt (Union[bytes, bytearray]): Salt to use.
-            keySize (int, optional): Output key length in bytes. Defaults to 32.
-            ivSize (int, optional): Output Initialization Vector (IV) length in bytes. Defaults to 16.
-            iterations (int, optional): Number of iterations to perform. Defaults to 1.
-            hashAlgorithm (str, optional): Hash algorithm to use for the KDF. Defaults to 'md5'.
-        Returns:
-            key, iv: Derived key and Initialization Vector (IV) bytes.
-
-        Taken from: https://gist.github.com/rafiibrahim8/0cd0f8c46896cafef6486cb1a50a16d3
-        OpenSSL original code: https://github.com/openssl/openssl/blob/master/crypto/evp/evp_key.c#L78
-        """
-
-        assert iterations > 0, "Iterations can not be less than 1."
-
-        if isinstance(password, str):
-            password = password.encode("utf-8")
-
-        final_length = keySize + ivSize
-        key_iv = b""
-        block = None
-
-        while len(key_iv) < final_length:
-            hasher = hashlib.new(hashAlgorithm)
-            if block:
-                hasher.update(block)
-            hasher.update(password)
-            hasher.update(salt)
-            block = hasher.digest()
-            for _ in range(1, iterations):
-                block = hashlib.new(hashAlgorithm, block).digest()
-            key_iv += block
-
-        key, iv = key_iv[:keySize], key_iv[keySize:final_length]
-        return key, iv
-
-    def _decrypt(encrypted_stores, password, key, iv):
-        if usePycryptodome:
-            cipher = AES.new(key, AES.MODE_CBC, iv=iv)
-            plaintext = cipher.decrypt(encrypted_stores)
-            plaintext = unpad(plaintext, 16, style="pkcs7")
-        else:
-            cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
-            decryptor = cipher.decryptor()
-            plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
-            unpadder = padding.PKCS7(128).unpadder()
-            plaintext = unpadder.update(plaintext) + unpadder.finalize()
-            plaintext = plaintext.decode("utf-8")
-        return plaintext
-
-    if not password is None:
-        try:
-            key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
-        except:
-            raise Exception("yfinance failed to decrypt Yahoo data response")
-        plaintext = _decrypt(encrypted_stores, password, key, iv)
-    else:
-        success = False
-        for i in range(len(candidate_passwords)):
-            # print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
-            password = candidate_passwords[i]
-            try:
-                key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
-
-                plaintext = _decrypt(encrypted_stores, password, key, iv)
-
-                success = True
-                break
-            except:
-                pass
-        if not success:
-            raise Exception("yfinance failed to decrypt Yahoo data response")
-
-    decoded_stores = json.loads(plaintext)
-    return decoded_stores
-
-
-_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
-
-
 class TickerData:
    """
    Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
@@ -204,7 +65,7 @@ class TickerData:
    def _get_proxy(self, proxy):
        # setup proxy in requests format
        if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
+            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
                proxy = proxy["https"]
            proxy = {"https": proxy}
        return proxy
@@ -213,124 +74,3 @@ class TickerData:
        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
        response.raise_for_status()
        return response.json()
-
-    def _get_decryption_keys_from_yahoo_js(self, soup):
-        result = None
-
-        key_count = 4
-        re_script = soup.find("script", string=re.compile("root.App.main")).text
-        re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
-        re_data.pop("context", None)
-        key_list = list(re_data.keys())
-        if re_data.get("plugins"):  # 1) attempt to get last 4 keys after plugins
-            ind = key_list.index("plugins")
-            if len(key_list) > ind+1:
-                sub_keys = key_list[ind+1:]
-                if len(sub_keys) == key_count:
-                    re_obj = {}
-                    missing_val = False
-                    for k in sub_keys:
-                        if not re_data.get(k):
-                            missing_val = True
-                            break
-                        re_obj.update({k: re_data.get(k)})
-                    if not missing_val:
-                        result = re_obj
-
-        if not result is None:
-            return [''.join(result.values())]
-
-        re_keys = []    # 2) attempt scan main.js file approach to get keys
-        prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
-        tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
-        for t in tags:
-            response_js = self.cache_get(t)
-            #
-            if response_js.status_code != 200:
-                time.sleep(random.randrange(10, 20))
-                response_js.close()
-            else:
-                r_data = response_js.content.decode("utf8")
-                re_list = [
-                    x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
-                ]
-                for rl in re_list:
-                    re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
-                    if len(re_sublist) == key_count:
-                        re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
-                        break
-                response_js.close()
-            if len(re_keys) == key_count:
-                break
-        if len(re_keys) > 0:
-            re_obj = {}
-            missing_val = False
-            for k in re_keys:
-                if not re_data.get(k):
-                    missing_val = True
-                    break
-                re_obj.update({k: re_data.get(k)})
-            if not missing_val:
-                return [''.join(re_obj.values())]
-
-        return []
-
-    @lru_cache_freezeargs
-    @lru_cache(maxsize=cache_maxsize)
-    def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
-        '''
-        get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
-        '''
-        if sub_page:
-            ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
-        else:
-            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
-
-        response = self.get(url=ticker_url, proxy=proxy)
-        html = response.text
-
-        # The actual json-data for stores is in a javascript assignment in the webpage
-        try:
-            json_str = html.split('root.App.main =')[1].split(
-                '(this)')[0].split(';\n}')[0].strip()
-        except IndexError:
-            # Fetch failed, probably because Yahoo spam triggered
-            return {}
-
-        data = json.loads(json_str)
-
-        # Gather decryption keys:
-        soup = BeautifulSoup(response.content, "html.parser")
-        keys = self._get_decryption_keys_from_yahoo_js(soup)
-        if len(keys) == 0:
-            msg = "No decryption keys could be extracted from JS file."
-            if "requests_cache" in str(type(response)):
-                msg += " Try flushing your 'requests_cache', probably parsing old JS."
-            logger.warning("%s Falling back to backup decrypt methods.", msg)
-        if len(keys) == 0:
-            keys = []
-            try:
-                extra_keys = _extract_extra_keys_from_stores(data)
-                keys = [''.join(extra_keys[-4:])]
-            except:
-                pass
-            #
-            keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
-            response_gh = self.cache_get(keys_url)
-            keys += response_gh.text.splitlines()
-
-        # Decrypt!
-        stores = decrypt_cryptojs_aes_stores(data, keys)
-        if stores is None:
-            # Maybe Yahoo returned old format, not encrypted
-            if "context" in data and "dispatcher" in data["context"]:
-                stores = data['context']['dispatcher']['stores']
-        if stores is None:
-            raise Exception(f"{self.ticker}: Failed to extract data stores from web request")
-
-        # return data
-        new_data = json.dumps(stores).replace('{}', 'null')
-        new_data = re.sub(
-            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
-
-        return json.loads(new_data)
--- a/yfinance/exceptions.py
+++ b/yfinance/exceptions.py
@@ -4,3 +4,9 @@ class YFinanceException(Exception):

 class YFinanceDataException(YFinanceException):
    pass
+
+
+class YFNotImplementedError(NotImplementedError):
+    def __init__(self, method_name):
+        super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
+
--- a/yfinance/multi.py
+++ b/yfinance/multi.py
@@ -22,14 +22,17 @@
 from __future__ import print_function

 import logging
-import traceback
 import time as _time
+import traceback
+
 import multitasking as _multitasking
 import pandas as _pd

 from . import Ticker, utils
 from . import shared

+
+@utils.log_indent_decorator
 def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
             group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
@@ -46,7 +49,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            Intraday data cannot extend last 60 days
        start: str
            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
-            Default is 1900-01-01
+            Default is 99 years ago
            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
        end: str
            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
@@ -85,14 +88,26 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        session: None or Session
            Optional. Pass your own session object to be used for all requests
    """
+    logger = utils.get_yf_logger()

    if show_errors is not None:
        if show_errors:
            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
-            logging.getLogger('yfinance').setLevel(logging.ERROR)
+            logger.setLevel(logging.ERROR)
        else:
            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
-            logging.getLogger('yfinance').setLevel(logging.CRITICAL)
+            logger.setLevel(logging.CRITICAL)
+
+    if logger.isEnabledFor(logging.DEBUG):
+        if threads:
+            # With DEBUG, each thread generates a lot of log messages.
+            # And with multi-threading, these messages will be interleaved, bad!
+            # So disable multi-threading to make log readable.
+            logger.debug('Disabling multithreading because DEBUG logging enabled')
+            threads = False
+        if progress:
+            # Disable progress bar, interferes with display of log messages
+            progress = False

    if ignore_tz is None:
        # Set default value depending on interval
@@ -167,7 +182,8 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        errors = {}
        for ticker in shared._ERRORS:
            err = shared._ERRORS[ticker]
-            if not err in errors:
+            err = err.replace(f'{ticker}', '%ticker%')
+            if err not in errors:
                errors[err] = [ticker]
            else:
                errors[err].append(ticker)
@@ -178,7 +194,8 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        tbs = {}
        for ticker in shared._TRACEBACKS:
            tb = shared._TRACEBACKS[ticker]
-            if not tb in tbs:
+            tb = tb.replace(f'{ticker}', '%ticker%')
+            if tb not in tbs:
                tbs[tb] = [ticker]
            else:
                tbs[tb].append(ticker)
@@ -192,7 +209,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_

    if len(tickers) == 1:
        ticker = tickers[0]
-        return shared._DFS[shared._ISINS.get(ticker, ticker)]
+        return shared._DFS[ticker]

    try:
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -2,6 +2,7 @@ import pandas as pd

 from yfinance import utils
 from yfinance.data import TickerData
+from yfinance.exceptions import YFNotImplementedError


 class Analysis:
@@ -20,99 +21,29 @@ class Analysis:
    @property
    def earnings_trend(self) -> pd.DataFrame:
        if self._earnings_trend is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('earnings_trend')
        return self._earnings_trend

    @property
    def analyst_trend_details(self) -> pd.DataFrame:
        if self._analyst_trend_details is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_trend_details')
        return self._analyst_trend_details

    @property
    def analyst_price_target(self) -> pd.DataFrame:
        if self._analyst_price_target is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_price_target')
        return self._analyst_price_target

    @property
    def rev_est(self) -> pd.DataFrame:
        if self._rev_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('rev_est')
        return self._rev_est

    @property
    def eps_est(self) -> pd.DataFrame:
        if self._eps_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('eps_est')
        return self._eps_est
-
-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # Analysis Data/Analyst Forecasts
-        analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
-        try:
-            analysis_data = analysis_data['QuoteSummaryStore']
-        except KeyError as e:
-            err_msg = "No analysis data found, symbol may be delisted"
-            logger.error('%s: %s', self._data.ticker, err_msg)
-            return
-
-        if isinstance(analysis_data.get('earningsTrend'), dict):
-            try:
-                analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
-                analysis['endDate'] = pd.to_datetime(analysis['endDate'])
-                analysis.set_index('period', inplace=True)
-                analysis.index = analysis.index.str.upper()
-                analysis.index.name = 'Period'
-                analysis.columns = utils.camel2title(analysis.columns)
-
-                dict_cols = []
-
-                for idx, row in analysis.iterrows():
-                    for colname, colval in row.items():
-                        if isinstance(colval, dict):
-                            dict_cols.append(colname)
-                            for k, v in colval.items():
-                                new_colname = colname + ' ' + \
-                                              utils.camel2title([k])[0]
-                                analysis.loc[idx, new_colname] = v
-
-                self._earnings_trend = analysis[[
-                    c for c in analysis.columns if c not in dict_cols]]
-            except Exception:
-                pass
-
-        try:
-            self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
-        except Exception as e:
-            self._analyst_trend_details = None
-        try:
-            self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
-                ['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
-        except Exception as e:
-            self._analyst_price_target = None
-        earnings_estimate = []
-        revenue_estimate = []
-        if self._analyst_trend_details is not None :
-            for key in analysis_data['earningsTrend']['trend']:
-                try:
-                    earnings_dict = key['earningsEstimate']
-                    earnings_dict['period'] = key['period']
-                    earnings_dict['endDate'] = key['endDate']
-                    earnings_estimate.append(earnings_dict)
-
-                    revenue_dict = key['revenueEstimate']
-                    revenue_dict['period'] = key['period']
-                    revenue_dict['endDate'] = key['endDate']
-                    revenue_estimate.append(revenue_dict)
-                except Exception as e:
-                    pass
-            self._rev_est = pd.DataFrame(revenue_estimate)
-            self._eps_est = pd.DataFrame(earnings_estimate)
-        else:
-            self._rev_est = pd.DataFrame()
-            self._eps_est = pd.DataFrame()
--- a/yfinance/scrapers/fundamentals.py
+++ b/yfinance/scrapers/fundamentals.py
@@ -1,15 +1,12 @@
 import datetime
-import logging
 import json

 import pandas as pd
-import numpy as np

-from yfinance import utils
+from yfinance import utils, const
 from yfinance.data import TickerData
-from yfinance.exceptions import YFinanceDataException, YFinanceException
+from yfinance.exceptions import YFinanceException, YFNotImplementedError

-logger = utils.get_yf_logger()

 class Fundamentals:

@@ -33,71 +30,15 @@ class Fundamentals:
    @property
    def earnings(self) -> dict:
        if self._earnings is None:
-            self._scrape_earnings(self.proxy)
+            raise YFNotImplementedError('earnings')
        return self._earnings

    @property
    def shares(self) -> pd.DataFrame:
        if self._shares is None:
-            self._scrape_shares(self.proxy)
+            raise YFNotImplementedError('shares')
        return self._shares

-    def _scrape_basics(self, proxy):
-        if self._basics_already_scraped:
-            return
-        self._basics_already_scraped = True
-
-        self._financials_data = self._data.get_json_data_stores('financials', proxy)
-        try:
-            self._fin_data_quote = self._financials_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No financials data found, symbol may be delisted"
-            logger.error('%s: %s', self._data.ticker, err_msg)
-            return None
-
-    def _scrape_earnings(self, proxy):
-        self._scrape_basics(proxy)
-        # earnings
-        self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
-        if self._fin_data_quote is None:
-            return
-        if isinstance(self._fin_data_quote.get('earnings'), dict):
-            try:
-                earnings = self._fin_data_quote['earnings']['financialsChart']
-                earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
-                self._earnings['financialCurrency'] = earnings['financialCurrency']
-                df = pd.DataFrame(earnings['yearly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Year'
-                self._earnings['yearly'] = df
-
-                df = pd.DataFrame(earnings['quarterly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Quarter'
-                self._earnings['quarterly'] = df
-            except Exception:
-                pass
-
-    def _scrape_shares(self, proxy):
-        self._scrape_basics(proxy)
-        # shares outstanding
-        try:
-            # keep only years with non None data
-            available_shares = [shares_data for shares_data in
-                                self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
-                                if
-                                shares_data]
-            shares = pd.DataFrame(available_shares)
-            shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
-            shares.set_index('Year', inplace=True)
-            shares.drop(columns=['dataId', 'asOfDate',
-                                 'periodType', 'currencyCode'], inplace=True)
-            shares.rename(
-                columns={'reportedValue': "BasicShares"}, inplace=True)
-            self._shares = shares
-        except Exception:
-            pass
-

 class Financials:
    def __init__(self, data: TickerData):
@@ -105,28 +46,26 @@ class Financials:
        self._income_time_series = {}
        self._balance_sheet_time_series = {}
        self._cash_flow_time_series = {}
-        self._income_scraped = {}
-        self._balance_sheet_scraped = {}
-        self._cash_flow_scraped = {}

    def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
        res = self._income_time_series
        if freq not in res:
-            res[freq] = self._fetch_time_series("income", freq, proxy=None)
+            res[freq] = self._fetch_time_series("income", freq, proxy)
        return res[freq]

    def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
        res = self._balance_sheet_time_series
        if freq not in res:
-            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy=None)
+            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
        return res[freq]

    def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
        res = self._cash_flow_time_series
        if freq not in res:
-            res[freq] = self._fetch_time_series("cash-flow", freq, proxy=None)
+            res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
        return res[freq]

+    @utils.log_indent_decorator
    def _fetch_time_series(self, name, timescale, proxy=None):
        # Fetching time series preferred over scraping 'QuoteSummaryStore',
        # because it matches what Yahoo shows. But for some tickers returns nothing, 
@@ -136,9 +75,9 @@ class Financials:
        allowed_timescales = ["yearly", "quarterly"]

        if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
        if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")

        try:
            statement = self._create_financials_table(name, timescale, proxy)
@@ -146,7 +85,7 @@ class Financials:
            if statement is not None:
                return statement
        except YFinanceException as e:
-            logger.error("%s: Failed to create %s financials table for reason: %r", self._data.ticker, name, e)
+            utils.get_yf_logger().error(f"{self._data.ticker}: Failed to create {name} financials table for reason: {e}")
        return pd.DataFrame()

    def _create_financials_table(self, name, timescale, proxy):
@@ -154,51 +93,24 @@ class Financials:
            # Yahoo stores the 'income' table internally under 'financials' key
            name = "financials"

-        keys = self._get_datastore_keys(name, proxy)
+        keys = const.fundamentals_keys[name]
+
        try:
            return self.get_financials_time_series(timescale, keys, proxy)
        except Exception as e:
            pass

-    def _get_datastore_keys(self, sub_page, proxy) -> list:
-        data_stores = self._data.get_json_data_stores(sub_page, proxy)
-
-        # Step 1: get the keys:
-        def _finditem1(key, obj):
-            values = []
-            if isinstance(obj, dict):
-                if key in obj.keys():
-                    values.append(obj[key])
-                for k, v in obj.items():
-                    values += _finditem1(key, v)
-            elif isinstance(obj, list):
-                for v in obj:
-                    values += _finditem1(key, v)
-            return values
-
-        try:
-            keys = _finditem1("key", data_stores['FinancialTemplateStore'])
-        except KeyError as e:
-            raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
-
-        if not keys:
-            raise YFinanceDataException("No keys in FinancialTemplateStore")
-        return keys
-
    def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
        timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
        timescale = timescale_translation[timescale]

        # Step 2: construct url:
-        ts_url_base = \
-            "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
-                .format(self._data.ticker)
-
+        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
        url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
        # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
        start_dt = datetime.datetime(2016, 12, 31)
        end = pd.Timestamp.utcnow().ceil("D")
-        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
+        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"

        # Step 3: fetch and reshape data
        json_str = self._data.cache_get(url=url, proxy=proxy).text
@@ -233,89 +145,3 @@ class Financials:
        df = df[sorted(df.columns, reverse=True)]

        return df
-
-    def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._income_scraped
-        if freq not in res:
-            res[freq] = self._scrape("income", freq, proxy=None)
-        return res[freq]
-
-    def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._balance_sheet_scraped
-        if freq not in res:
-            res[freq] = self._scrape("balance-sheet", freq, proxy=None)
-        return res[freq]
-
-    def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._cash_flow_scraped
-        if freq not in res:
-            res[freq] = self._scrape("cash-flow", freq, proxy=None)
-        return res[freq]
-
-    def _scrape(self, name, timescale, proxy=None):
-        # Backup in case _fetch_time_series() fails to return data
-
-        allowed_names = ["income", "balance-sheet", "cash-flow"]
-        allowed_timescales = ["yearly", "quarterly"]
-
-        if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
-        if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
-
-        try:
-            statement = self._create_financials_table_old(name, timescale, proxy)
-
-            if statement is not None:
-                return statement
-        except YFinanceException as e:
-            logger.error("%s: Failed to create financials table for %s reason: %r", self._data.ticker, name, e)
-        return pd.DataFrame()
-
-    def _create_financials_table_old(self, name, timescale, proxy):
-        data_stores = self._data.get_json_data_stores("financials", proxy)
-
-        # Fetch raw data
-        if not "QuoteSummaryStore" in data_stores:
-            raise YFinanceDataException(f"Yahoo not returning legacy financials data")
-        data = data_stores["QuoteSummaryStore"]
-
-        if name == "cash-flow":
-            key1 = "cashflowStatement"
-            key2 = "cashflowStatements"
-        elif name == "balance-sheet":
-            key1 = "balanceSheet"
-            key2 = "balanceSheetStatements"
-        else:
-            key1 = "incomeStatement"
-            key2 = "incomeStatementHistory"
-        key1 += "History"
-        if timescale == "quarterly":
-            key1 += "Quarterly"
-        if key1 not in data or data[key1] is None or key2 not in data[key1]:
-            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
-        data = data[key1][key2]
-
-        # Tabulate
-        df = pd.DataFrame(data)
-        if len(df) == 0:
-            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
-        df = df.drop(columns=['maxAge'])
-        for col in df.columns:
-            df[col] = df[col].replace('-', np.nan)
-        df.set_index('endDate', inplace=True)
-        try:
-            df.index = pd.to_datetime(df.index, unit='s')
-        except ValueError:
-            df.index = pd.to_datetime(df.index)
-        df = df.T
-        df.columns.name = ''
-        df.index.name = 'Breakdown'
-        # rename incorrect yahoo key
-        df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
-
-        # Upper-case first letter, leave rest unchanged:
-        s0 = df.index[0]
-        df.index = [s[0].upper()+s[1:] for s in df.index]
-
-        return df
--- a/yfinance/scrapers/holders.py
+++ b/yfinance/scrapers/holders.py
@@ -2,6 +2,7 @@ import pandas as pd

 from yfinance.data import TickerData

+
 class Holders:
    _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'

@@ -32,9 +33,9 @@ class Holders:
        return self._mutualfund

    def _scrape(self, proxy):
-        ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
+        ticker_url = f"{self._SCRAPE_URL_}/{self._data.ticker}"
        try:
-            resp = self._data.cache_get(ticker_url + '/holders', proxy)
+            resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
            holders = pd.read_html(resp.text)
        except Exception:
            holders = []
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -1,15 +1,15 @@
 import datetime
-import logging
 import json
+import logging
 import warnings
+from collections.abc import MutableMapping

-import pandas as pd
 import numpy as _np
+import pandas as pd

 from yfinance import utils
 from yfinance.data import TickerData
-
-logger = utils.get_yf_logger()
+from yfinance.exceptions import YFNotImplementedError

 info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
 info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
@@ -21,12 +21,9 @@ info_retired_keys_symbol = {"symbol"}
 info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol


-PRUNE_INFO = True
-# PRUNE_INFO = False
-_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"


-from collections.abc import MutableMapping
 class InfoDictWrapper(MutableMapping):
    """ Simple wrapper around info dict, intercepting 'gets' to 
    print how-to-migrate messages for specific keys. Requires
@@ -81,10 +78,9 @@ class InfoDictWrapper(MutableMapping):
 class FastInfo:
    # Contain small subset of info[] items that can be fetched faster elsewhere.
    # Imitates a dict.
-    def __init__(self, tickerBaseObject):
-        utils.print_once("yfinance: Note: 'Ticker.info' dict is now fixed & improved, 'fast_info' is no longer faster")
-
+    def __init__(self, tickerBaseObject, proxy=None):
        self._tkr = tickerBaseObject
+        self.proxy = proxy

        self._prices_1y = None
        self._prices_1wk_1h_prepost = None
@@ -131,12 +127,12 @@ class FastInfo:

        # Because released before fixing key case, need to officially support 
        # camel-case but also secretly support snake-case
-        base_keys = [k for k in _properties if not '_' in k]
+        base_keys = [k for k in _properties if '_' not in k]

        sc_keys = [k for k in _properties if '_' in k]

-        self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
-        self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
+        self._sc_to_cc_key = {k: utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v: k for k, v in self._sc_to_cc_key.items()}
 
        self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
        self._keys = sorted(self._public_keys + sc_keys)
@@ -144,52 +140,58 @@ class FastInfo:
    # dict imitation:
    def keys(self):
        return self._public_keys
+
    def items(self):
-        return [(k,self[k]) for k in self._public_keys]
+        return [(k, self[k]) for k in self._public_keys]
+
    def values(self):
        return [self[k] for k in self._public_keys]
+
    def get(self, key, default=None):
        if key in self.keys():
            if key in self._cc_to_sc_key:
                key = self._cc_to_sc_key[key]
            return self[key]
        return default
+
    def __getitem__(self, k):
        if not isinstance(k, str):
            raise KeyError(f"key must be a string")
-        if not k in self._keys:
+        if k not in self._keys:
            raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
        if k in self._cc_to_sc_key:
            k = self._cc_to_sc_key[k]
        return getattr(self, k)
+
    def __contains__(self, k):
        return k in self.keys()
+
    def __iter__(self):
        return iter(self.keys())

    def __str__(self):
        return "lazy-loading dict with keys = " + str(self.keys())
+
    def __repr__(self):
        return self.__str__()

    def toJSON(self, indent=4):
-        d = {k:self[k] for k in self.keys()}
-        return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
+        d = {k: self[k] for k in self.keys()}
+        return json.dumps({k: self[k] for k in self.keys()}, indent=indent)

    def _get_1y_prices(self, fullDaysOnly=False):
        if self._prices_1y is None:
            # Temporarily disable error printing
-            l = logger.level
-            logger.setLevel(logging.CRITICAL)
-            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True)
-            logger.setLevel(l)
-            self._md = self._tkr.get_history_metadata()
+            logging.disable(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+            self._md = self._tkr.get_history_metadata(proxy=self.proxy)
            try:
                ctp = self._md["currentTradingPeriod"]
                self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
                self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
                self._today_midnight = self._today_close.ceil("D")
-            except:
+            except Exception:
                self._today_open = None
                self._today_close = None
                self._today_midnight = None
@@ -209,19 +211,17 @@ class FastInfo:
    def _get_1wk_1h_prepost_prices(self):
        if self._prices_1wk_1h_prepost is None:
            # Temporarily disable error printing
-            l = logger.level
-            logger.setLevel(logging.CRITICAL)
-            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True)
-            logger.setLevel(l)
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
        return self._prices_1wk_1h_prepost

    def _get_1wk_1h_reg_prices(self):
        if self._prices_1wk_1h_reg is None:
            # Temporarily disable error printing
-            l = logger.level
-            logger.setLevel(logging.CRITICAL)
-            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False)
-            logger.setLevel(l)
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
        return self._prices_1wk_1h_reg

    def _get_exchange_metadata(self):
@@ -229,7 +229,7 @@ class FastInfo:
            return self._md

        self._get_1y_prices()
-        self._md = self._tkr.get_history_metadata()
+        self._md = self._tkr.get_history_metadata(proxy=self.proxy)
        return self._md

    def _exchange_open_now(self):
@@ -262,7 +262,7 @@ class FastInfo:

        if self._tkr._history_metadata is None:
            self._get_1y_prices()
-        md = self._tkr.get_history_metadata()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
        self._currency = md["currency"]
        return self._currency

@@ -273,7 +273,7 @@ class FastInfo:

        if self._tkr._history_metadata is None:
            self._get_1y_prices()
-        md = self._tkr.get_history_metadata()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
        self._quote_type = md["instrumentType"]
        return self._quote_type

@@ -298,10 +298,10 @@ class FastInfo:
        if self._shares is not None:
            return self._shares

-        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548))
-        if shares is None:
-            # Requesting 18 months failed, so fallback to shares which should include last year
-            shares = self._tkr.get_shares()
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548), proxy=self.proxy)
+        # if shares is None:
+        #     # Requesting 18 months failed, so fallback to shares which should include last year
+        #     shares = self._tkr.get_shares()
        if shares is not None:
            if isinstance(shares, pd.DataFrame):
                shares = shares[shares.columns[0]]
@@ -568,9 +568,7 @@ class Quote:
    @property
    def info(self) -> dict:
        if self._info is None:
-            # self._scrape(self.proxy)  # decrypt broken
            self._fetch(self.proxy)
-
            self._fetch_complementary(self.proxy)

        return self._info
@@ -578,150 +576,29 @@ class Quote:
    @property
    def sustainability(self) -> pd.DataFrame:
        if self._sustainability is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('sustainability')
        return self._sustainability

    @property
    def recommendations(self) -> pd.DataFrame:
        if self._recommendations is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('recommendations')
        return self._recommendations

    @property
    def calendar(self) -> pd.DataFrame:
        if self._calendar is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('calendar')
        return self._calendar

-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # get info and sustainability
-        json_data = self._data.get_json_data_stores(proxy=proxy)
-        try:
-            quote_summary_store = json_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No summary info found, symbol may be delisted"
-            logger.error('%s: %s', self._data.ticker, err_msg)
-            return None
-
-        # sustainability
-        d = {}
-        try:
-            if isinstance(quote_summary_store.get('esgScores'), dict):
-                for item in quote_summary_store['esgScores']:
-                    if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
-                        d[item] = quote_summary_store['esgScores'][item]
-
-                s = pd.DataFrame(index=[0], data=d)[-1:].T
-                s.columns = ['Value']
-                s.index.name = '%.f-%.f' % (
-                    s[s.index == 'ratingYear']['Value'].values[0],
-                    s[s.index == 'ratingMonth']['Value'].values[0])
-
-                self._sustainability = s[~s.index.isin(
-                    ['maxAge', 'ratingYear', 'ratingMonth'])]
-        except Exception:
-            pass
-
-        self._info = {}
-        try:
-            items = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
-            for item in items:
-                if isinstance(quote_summary_store.get(item), dict):
-                    self._info.update(quote_summary_store[item])
-        except Exception:
-            pass
-
-        # For ETFs, provide this valuable data: the top holdings of the ETF
-        try:
-            if 'topHoldings' in quote_summary_store:
-                self._info.update(quote_summary_store['topHoldings'])
-        except Exception:
-            pass
-
-        try:
-            if not isinstance(quote_summary_store.get('summaryDetail'), dict):
-                # For some reason summaryDetail did not give any results. The price dict
-                # usually has most of the same info
-                self._info.update(quote_summary_store.get('price', {}))
-        except Exception:
-            pass
-
-        try:
-            # self._info['regularMarketPrice'] = self._info['regularMarketOpen']
-            self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'regularMarketPrice', self._info.get('regularMarketOpen', None))
-        except Exception:
-            pass
-
-        try:
-            self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'preMarketPrice', self._info.get('preMarketPrice', None))
-        except Exception:
-            pass
-
-        self._info['logo_url'] = ""
-        try:
-            if not 'website' in self._info:
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
-                                         self._info['shortName'].split(' ')[0].split(',')[0]
-            else:
-                domain = self._info['website'].split(
-                    '://')[1].split('/')[0].replace('www.', '')
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
-        except Exception:
-            pass
-
-        # Delete redundant info[] keys, because values can be accessed faster
-        # elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
-        # But record the dropped keys, because in rare cases they are needed.
-        self._retired_info = {}
-        for k in info_retired_keys:
-            if k in self._info:
-                self._retired_info[k] = self._info[k]
-                if PRUNE_INFO:
-                    del self._info[k]
-        if PRUNE_INFO:
-            # InfoDictWrapper will explain how to access above data elsewhere
-            self._info = InfoDictWrapper(self._info)
-
-        # events
-        try:
-            cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
-            cal['earningsDate'] = pd.to_datetime(
-                cal['earningsDate'], unit='s')
-            self._calendar = cal.T
-            self._calendar.index = utils.camel2title(self._calendar.index)
-            self._calendar.columns = ['Value']
-        except Exception as e:
-            pass
-
-        # analyst recommendations
-        try:
-            rec = pd.DataFrame(
-                quote_summary_store['upgradeDowngradeHistory']['history'])
-            rec['earningsDate'] = pd.to_datetime(
-                rec['epochGradeDate'], unit='s')
-            rec.set_index('earningsDate', inplace=True)
-            rec.index.name = 'Date'
-            rec.columns = utils.camel2title(rec.columns)
-            self._recommendations = rec[[
-                'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
-        except Exception:
-            pass
-
    def _fetch(self, proxy):
        if self._already_fetched:
            return
        self._already_fetched = True
-        modules = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        params_dict = {"modules": modules, "ssl": "true"}
        result = self._data.get_raw_json(
-            _BASIC_URL_ + f"/{self._data.ticker}", params={"modules": ",".join(modules), "ssl": "true"}, proxy=proxy
+            _BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
        )
        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
        query1_info = next(
@@ -741,13 +618,14 @@ class Quote:
            if v1
        }
        # recursively format but only because of 'companyOfficers'
+
        def _format(k, v):
            if isinstance(v, dict) and "raw" in v and "fmt" in v:
                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
            elif isinstance(v, list):
                v2 = [_format(None, x) for x in v]
            elif isinstance(v, dict):
-                v2 = {k:_format(k, x) for k, x in v.items()}
+                v2 = {k: _format(k, x) for k, x in v.items()}
            elif isinstance(v, str):
                v2 = v.replace("\xa0", " ")
            else:
@@ -792,8 +670,7 @@ class Quote:
            #     pass
            #
            # For just one/few variable is faster to query directly:
-            url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
-                self._data.ticker, self._data.ticker)
+            url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
            for k in keys:
                url += "&type=" + k
            # Request 6 months of data
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -22,10 +22,10 @@
 from __future__ import print_function

 import datetime as _datetime
-import pandas as _pd
-
 from collections import namedtuple as _namedtuple

+import pandas as _pd
+
 from .base import TickerBase


@@ -33,25 +33,29 @@ class Ticker(TickerBase):
    def __init__(self, ticker, session=None):
        super(Ticker, self).__init__(ticker, session=session)
        self._expirations = {}
+        self._underlying  = {}

    def __repr__(self):
-        return 'yfinance.Ticker object <%s>' % self.ticker
+        return f'yfinance.Ticker object <{self.ticker}>'

    def _download_options(self, date=None, proxy=None):
        if date is None:
-            url = "{}/v7/finance/options/{}".format(
-                self._base_url, self.ticker)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}"
        else:
-            url = "{}/v7/finance/options/{}?date={}".format(
-                self._base_url, self.ticker, date)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}?date={date}"

        r = self._data.get(url=url, proxy=proxy).json()
        if len(r.get('optionChain', {}).get('result', [])) > 0:
            for exp in r['optionChain']['result'][0]['expirationDates']:
                self._expirations[_datetime.datetime.utcfromtimestamp(
                    exp).strftime('%Y-%m-%d')] = exp
+
+            self._underlying = r['optionChain']['result'][0].get('quote', {})
+
            opt = r['optionChain']['result'][0].get('options', [])
-            return opt[0] if len(opt) > 0 else []
+
+            return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
+        return {}

    def _options2df(self, opt, tz=None):
        data = _pd.DataFrame(opt).reindex(columns=[
@@ -84,15 +88,15 @@ class Ticker(TickerBase):
                self._download_options()
            if date not in self._expirations:
                raise ValueError(
-                    "Expiration `%s` cannot be found. "
-                    "Available expiration are: [%s]" % (
-                        date, ', '.join(self._expirations)))
+                    f"Expiration `{date}` cannot be found. "
+                    f"Available expirations are: [{', '.join(self._expirations)}]")
            date = self._expirations[date]
            options = self._download_options(date, proxy=proxy)

-        return _namedtuple('Options', ['calls', 'puts'])(**{
+        return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
            "calls": self._options2df(options['calls'], tz=tz),
-            "puts": self._options2df(options['puts'], tz=tz)
+            "puts": self._options2df(options['puts'], tz=tz),
+            "underlying": options['underlying']
        })

    # ------------------------
@@ -137,6 +141,10 @@ class Ticker(TickerBase):
    def info(self) -> dict:
        return self.get_info()

+    @property
+    def fast_info(self):
+        return self.get_fast_info()
+
    @property
    def calendar(self) -> _pd.DataFrame:
        return self.get_calendar()
@@ -235,6 +243,10 @@ class Ticker(TickerBase):
    def news(self):
        return self.get_news()

+    @property
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
+
    @property
    def earnings_trend(self) -> _pd.DataFrame:
        return self.get_earnings_trend()
--- a/yfinance/tickers.py
+++ b/yfinance/tickers.py
@@ -22,19 +22,21 @@
 from __future__ import print_function

 from . import Ticker, multi
+
+
 # from collections import namedtuple as _namedtuple


 class Tickers:

    def __repr__(self):
-        return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
+        return f"yfinance.Tickers object <{','.join(self.symbols)}>"

    def __init__(self, tickers, session=None):
        tickers = tickers if isinstance(
            tickers, list) else tickers.replace(',', ' ').split()
        self.symbols = [ticker.upper() for ticker in tickers]
-        self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}
+        self.tickers = {ticker: Ticker(ticker, session=session) for ticker in self.symbols}

        # self.tickers = _namedtuple(
        #     "Tickers", ticker_objects.keys(), rename=True
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -21,27 +21,32 @@

 from __future__ import print_function

+import atexit as _atexit
+
 import datetime as _datetime
-import dateutil as _dateutil
+import logging
+import os as _os
+import re as _re
+import sqlite3 as _sqlite3
+import sys as _sys
+import threading
+from functools import lru_cache
+from inspect import getmembers
+from threading import Lock
+from types import FunctionType
 from typing import Dict, Union, List, Optional

+import appdirs as _ad
+import numpy as _np
+import pandas as _pd
 import pytz as _tz
 import requests as _requests
-import re as _re
-import pandas as _pd
-import numpy as _np
-import sys as _sys
-import os as _os
-import appdirs as _ad
-import sqlite3 as _sqlite3
-import atexit as _atexit
-from functools import lru_cache
-import logging
-
-from threading import Lock
-
+from dateutil.relativedelta import relativedelta
 from pytz import UnknownTimeZoneError

+from yfinance import const
+from .const import _BASE_URL_
+
 try:
    import ujson as _json
 except ImportError:
@@ -52,14 +57,12 @@ user_agent_headers = {


 # From https://stackoverflow.com/a/59128615
-from types import FunctionType
-from inspect import getmembers
 def attributes(obj):
    disallowed_names = {
-      name for name, value in getmembers(type(obj)) 
+        name for name, value in getmembers(type(obj))
        if isinstance(value, FunctionType)}
    return {
-      name: getattr(obj, name) for name in dir(obj) 
+        name: getattr(obj, name) for name in dir(obj)
        if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}


@@ -70,31 +73,127 @@ def print_once(msg):
    print(msg)


+# Logging
+# Note: most of this logic is adding indentation with function depth,
+#       so that DEBUG log is readable.
+class IndentLoggerAdapter(logging.LoggerAdapter):
+    def process(self, msg, kwargs):
+        if get_yf_logger().isEnabledFor(logging.DEBUG):
+            i = ' ' * self.extra['indent']
+            if not isinstance(msg, str):
+                msg = str(msg)
+            msg = '\n'.join([i + m for m in msg.split('\n')])
+        return msg, kwargs
+
+
+_indentation_level = threading.local()
+
+
+class IndentationContext:
+    def __init__(self, increment=1):
+        self.increment = increment
+
+    def __enter__(self):
+        _indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        _indentation_level.indent -= self.increment
+
+
+def get_indented_logger(name=None):
+    # Never cache the returned value! Will break indentation.
+    return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
+
+
+def log_indent_decorator(func):
+    def wrapper(*args, **kwargs):
+        logger = get_indented_logger('yfinance')
+        logger.debug(f'Entering {func.__name__}()')
+
+        with IndentationContext():
+            result = func(*args, **kwargs)
+
+        logger.debug(f'Exiting {func.__name__}()')
+        return result
+
+    return wrapper
+
+
+class MultiLineFormatter(logging.Formatter):
+    # The 'fmt' formatting further down is only applied to first line
+    # of log message, specifically the padding after %level%.
+    # For multi-line messages, need to manually copy over padding.
+    def __init__(self, fmt):
+        super().__init__(fmt)
+        # Extract amount of padding
+        match = _re.search(r'%\(levelname\)-(\d+)s', fmt)
+        self.level_length = int(match.group(1)) if match else 0
+
+    def format(self, record):
+        original = super().format(record)
+        lines = original.split('\n')
+        levelname = lines[0].split(' ')[0]
+        if len(lines) <= 1:
+            return original
+        else:
+            # Apply padding to all lines below first
+            formatted = [lines[0]]
+            if self.level_length == 0:
+                padding = ' ' * len(levelname)
+            else:
+                padding = ' ' * self.level_length
+            padding += ' '  # +1 for space between level and message
+            formatted.extend(padding + line for line in lines[1:])
+            return '\n'.join(formatted)
+
+
 yf_logger = None
+yf_log_indented = False
+
+
 def get_yf_logger():
    global yf_logger
    if yf_logger is None:
-        yf_logger = logging.getLogger("yfinance")
-        if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
-            # Add stream handler if user not already added one
-            h = logging.StreamHandler()
-            formatter = logging.Formatter(fmt='%(levelname)s %(message)s')
-            h.setFormatter(formatter)
-            yf_logger.addHandler(h)
+        yf_logger = logging.getLogger('yfinance')
+    global yf_log_indented
+    if yf_log_indented:
+        yf_logger = get_indented_logger('yfinance')
    return yf_logger


+def setup_debug_formatting():
+    global yf_logger
+    yf_logger = get_yf_logger()
+
+    if not yf_logger.isEnabledFor(logging.DEBUG):
+        yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
+        return
+
+    if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
+        h = logging.StreamHandler()
+        # Ensure different level strings don't interfere with indentation
+        formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
+        h.setFormatter(formatter)
+        yf_logger.addHandler(h)
+
+    global yf_log_indented
+    yf_log_indented = True
+
+
+def enable_debug_mode():
+    get_yf_logger().setLevel(logging.DEBUG)
+    setup_debug_formatting()
+
+
 def is_isin(string):
-    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
+    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9])$", string))


 def get_all_by_isin(isin, proxy=None, session=None):
    if not (is_isin(isin)):
        raise ValueError("Invalid ISIN number")
-
-    from .base import _BASE_URL_
    session = session or _requests
-    url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
+    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
    data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
    try:
        data = data.json()
@@ -146,7 +245,7 @@ def empty_earnings_dates_df():


 def build_template(data):
-    '''
+    """
    build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".

    Returns:
@@ -155,95 +254,80 @@ def build_template(data):
        - template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
        - level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.

-    '''
+    """
    template_ttm_order = []  # Save the TTM (Trailing Twelve Months) ordering to an object.
    template_annual_order = []  # Save the annual ordering to an object.
    template_order = []  # Save the ordering to an object (this can be utilized for quarterlies)
    level_detail = []  # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
-    for key in data['template']:
-        # Loop through the json to retreive the exact financial order whilst appending to the objects
-        template_ttm_order.append('trailing{}'.format(key['key']))
-        template_annual_order.append('annual{}'.format(key['key']))
-        template_order.append('{}'.format(key['key']))
-        level_detail.append(0)
-        if 'children' in key:
-            for child1 in key['children']:  # Level 1
-                template_ttm_order.append('trailing{}'.format(child1['key']))
-                template_annual_order.append('annual{}'.format(child1['key']))
-                template_order.append('{}'.format(child1['key']))
-                level_detail.append(1)
-                if 'children' in child1:
-                    for child2 in child1['children']:  # Level 2
-                        template_ttm_order.append('trailing{}'.format(child2['key']))
-                        template_annual_order.append('annual{}'.format(child2['key']))
-                        template_order.append('{}'.format(child2['key']))
-                        level_detail.append(2)
-                        if 'children' in child2:
-                            for child3 in child2['children']:  # Level 3
-                                template_ttm_order.append('trailing{}'.format(child3['key']))
-                                template_annual_order.append('annual{}'.format(child3['key']))
-                                template_order.append('{}'.format(child3['key']))
-                                level_detail.append(3)
-                                if 'children' in child3:
-                                    for child4 in child3['children']:  # Level 4
-                                        template_ttm_order.append('trailing{}'.format(child4['key']))
-                                        template_annual_order.append('annual{}'.format(child4['key']))
-                                        template_order.append('{}'.format(child4['key']))
-                                        level_detail.append(4)
-                                        if 'children' in child4:
-                                            for child5 in child4['children']:  # Level 5
-                                                template_ttm_order.append('trailing{}'.format(child5['key']))
-                                                template_annual_order.append('annual{}'.format(child5['key']))
-                                                template_order.append('{}'.format(child5['key']))
-                                                level_detail.append(5)
+
+    def traverse(node, level):
+        """
+        A recursive function that visits a node and its children.
+
+        Args:
+            node: The current node in the data structure.
+            level: The depth of the current node in the data structure.
+        """
+        if level > 5:  # Stop when level is above 5
+            return
+        template_ttm_order.append(f"trailing{node['key']}")
+        template_annual_order.append(f"annual{node['key']}")
+        template_order.append(f"{node['key']}")
+        level_detail.append(level)
+        if 'children' in node:  # Check if the node has children
+            for child in node['children']:  # If yes, traverse each child
+                traverse(child, level + 1)  # Increment the level by 1 for each child
+
+    for key in data['template']:  # Loop through the data
+        traverse(key, 0)  # Call the traverse function with initial level being 0
+
    return template_ttm_order, template_annual_order, template_order, level_detail


-def retreive_financial_details(data):
-    '''
-    retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
+def retrieve_financial_details(data):
+    """
+    retrieve_financial_details returns all of the available financial details under the
+    "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages:
+    "/financials", "/cash-flow" and "/balance-sheet".

    Returns:
        - TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
        - Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
-    '''
+    """
    TTM_dicts = []  # Save a dictionary object to store the TTM financials.
    Annual_dicts = []  # Save a dictionary object to store the Annual financials.
-    for key in data['timeSeries']:  # Loop through the time series data to grab the key financial figures.
+
+    for key, timeseries in data.get('timeSeries', {}).items():  # Loop through the time series data to grab the key financial figures.
        try:
-            if len(data['timeSeries'][key]) > 0:
-                time_series_dict = {}
-                time_series_dict['index'] = key
-                for each in data['timeSeries'][key]:  # Loop through the years
-                    if each == None:
+            if timeseries:
+                time_series_dict = {'index': key}
+                for each in timeseries:  # Loop through the years
+                    if not each:
                        continue
-                    else:
-                        time_series_dict[each['asOfDate']] = each['reportedValue']
-                    # time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
+                    time_series_dict[each.get('asOfDate')] = each.get('reportedValue')
                if 'trailing' in key:
                    TTM_dicts.append(time_series_dict)
                elif 'annual' in key:
                    Annual_dicts.append(time_series_dict)
-        except Exception as e:
-            pass
+        except KeyError as e:
+            print(f"An error occurred while processing the key: {e}")
    return TTM_dicts, Annual_dicts


 def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
-    '''
+    """
    format_annual_financial_statement formats any annual financial statement

    Returns:
        - _statement: A fully formatted annual financial statement in pandas dataframe.
-    '''
+    """
    Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
    Annual = Annual.reindex(annual_order)
    Annual.index = Annual.index.str.replace(r'annual', '')

    # Note: balance sheet is the only financial statement with no ttm detail
-    if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
-        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
-        TTM = TTM.reindex(ttm_order)
+    if ttm_dicts and ttm_order:
+        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index").reindex(ttm_order)
        # Add 'TTM' prefix to all column names, so if combined we can tell
        # the difference between actuals and TTM (similar to yahoo finance).
        TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
@@ -261,12 +345,12 @@ def format_annual_financial_statement(level_detail, annual_dicts, annual_order,


 def format_quarterly_financial_statement(_statement, level_detail, order):
-    '''
+    """
    format_quarterly_financial_statements formats any quarterly financial statement

    Returns:
        - _statement: A fully formatted quarterly financial statement in pandas dataframe.
-    '''
+    """
    _statement = _statement.reindex(order)
    _statement.index = camel2title(_statement.T)
    _statement['level_detail'] = level_detail
@@ -317,7 +401,7 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]

    # Apply str.title() to non-acronym words
    strings = [s.split(sep) for s in strings]
-    strings = [[j.title() if not j in acronyms else j for j in s] for s in strings]
+    strings = [[j.title() if j not in acronyms else j for j in s] for s in strings]
    strings = [sep.join(s) for s in strings]

    return strings
@@ -347,14 +431,14 @@ def _parse_user_dt(dt, exchange_tz):

 def _interval_to_timedelta(interval):
    if interval == "1mo":
-        return _dateutil.relativedelta.relativedelta(months=1)
+        return relativedelta(months=1)
    elif interval == "3mo":
-        return _dateutil.relativedelta.relativedelta(months=3)
+        return relativedelta(months=3)
    elif interval == "1y":
-        return _dateutil.relativedelta.relativedelta(years=1)
+        return relativedelta(years=1)
    elif interval == "1wk":
-        return _pd.Timedelta(days=7, unit='d')
-    else: 
+        return _pd.Timedelta(days=7)
+    else:
        return _pd.Timedelta(interval)


@@ -454,8 +538,7 @@ def parse_actions(data):
            splits.set_index("date", inplace=True)
            splits.index = _pd.to_datetime(splits.index, unit="s")
            splits.sort_index(inplace=True)
-            splits["Stock Splits"] = splits["numerator"] / \
-                                     splits["denominator"]
+            splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
            splits = splits[["Stock Splits"]]

    if dividends is None:
@@ -534,7 +617,7 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
            elif interval == "3mo":
                last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
            else:
-                last_rows_same_interval = (dt1-dt2) < _pd.Timedelta(interval)
+                last_rows_same_interval = (dt1 - dt2) < _pd.Timedelta(interval)

            if last_rows_same_interval:
                # Last two rows are within same interval
@@ -567,11 +650,6 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):


 def safe_merge_dfs(df_main, df_sub, interval):
-    # Carefully merge 'df_sub' onto 'df_main'
-    # If naive merge fails, try again with reindexing df_sub:
-    # 1) if interval is weekly or monthly, then try with index set to start of week/month
-    # 2) if still failing then manually search through df_main.index to reindex df_sub
-
    if df_sub.shape[0] == 0:
        raise Exception("No data to merge")

@@ -581,6 +659,72 @@ def safe_merge_dfs(df_main, df_sub, interval):
        raise Exception("Expected 1 data col")
    data_col = data_cols[0]

+    df_main = df_main.sort_index()
+    intraday = interval.endswith('m') or interval.endswith('s')
+
+    td = _interval_to_timedelta(interval)
+    if intraday:
+        # On some exchanges the event can occur before market open.
+        # Problem when combining with intraday data.
+        # Solution = use dates, not datetimes, to map/merge.
+        df_main['_date'] = df_main.index.date
+        df_sub['_date'] = df_sub.index.date
+        indices = _np.searchsorted(_np.append(df_main['_date'], [df_main['_date'].iloc[-1]+td]), df_sub['_date'], side='left')
+        df_main = df_main.drop('_date', axis=1)
+        df_sub = df_sub.drop('_date', axis=1)
+    else:
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any() and not intraday:
+        empty_row_data = {c:[_np.nan] for c in const.price_colnames}|{'Volume':[0]}
+        if interval == '1d':
+            # For 1d, add all out-of-range event dates
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                df_main = _pd.concat([df_main, empty_row], sort=True)
+        else:
+            # Else, only add out-of-range event dates if occurring in interval 
+            # immediately after last pricfe row
+            last_dt = df_main.index[-1]
+            next_interval_start_dt = last_dt + td
+            next_interval_end_dt = next_interval_start_dt + td
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                if next_interval_start_dt <= dt < next_interval_end_dt:
+                    new_dt = next_interval_start_dt
+                    get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                    empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                    df_main = _pd.concat([df_main, empty_row], sort=True)
+        df_main = df_main.sort_index()
+
+        # Re-calculate indices
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any():
+        if intraday or interval in ['1d', '1wk']:
+            raise Exception(f"The following '{data_col}' events are out-of-range, did not expect with interval {interval}: {df_sub.index[f_outOfRange]}")
+        get_yf_logger().debug(f'Discarding these {data_col} events:' + '\n' + str(df_sub[f_outOfRange]))
+        df_sub = df_sub[~f_outOfRange].copy()
+        indices = indices[~f_outOfRange]
+
    def _reindex_events(df, new_index, data_col_name):
        if len(new_index) == len(set(new_index)):
            # No duplicates, easy
@@ -598,110 +742,19 @@ def safe_merge_dfs(df_main, df_sub, interval):
            df = df.groupby("_NewIndex").prod()
            df.index.name = None
        else:
-            raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
+            raise Exception(f"New index contains duplicates but unsure how to aggregate for '{data_col_name}'")
        if "_NewIndex" in df.columns:
            df = df.drop("_NewIndex", axis=1)
        return df

-    df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join()
-    # Backdate all df_sub.index dates to start of week/month
-    if interval == "1wk":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
-    elif interval == "1mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
-    elif interval == "3mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
-    else:
-        new_index = None
-
-    if new_index is not None:
-        new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
-        df_sub = _reindex_events(df_sub, new_index, data_col)
-        df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join(). Manually check each df_sub.index date against df_main.index to
-    # find matching interval
-    df_sub = df_sub_backup.copy()
-    new_index = [-1] * df_sub.shape[0]
-    for i in range(df_sub.shape[0]):
-        dt_sub_i = df_sub.index[i]
-        if dt_sub_i in df_main.index:
-            new_index[i] = dt_sub_i
-            continue
-        # Found a bad index date, need to search for near-match in df_main (same week/month)
-        fixed = False
-        for j in range(df_main.shape[0] - 1):
-            dt_main_j0 = df_main.index[j]
-            dt_main_j1 = df_main.index[j + 1]
-            if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
-                fixed = True
-                if interval.endswith('h') or interval.endswith('m'):
-                    # Must also be same day
-                    fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
-                if fixed:
-                    dt_sub_i = dt_main_j0
-                    break
-        if not fixed:
-            last_main_dt = df_main.index[df_main.shape[0] - 1]
-            diff = dt_sub_i - last_main_dt
-            if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1wk":
-                if last_main_dt.week == dt_sub_i.week:
-                    dt_sub_i = last_main_dt
-                    fixed = True
-                elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
-                    # With some specific start dates (e.g. around early Jan), Yahoo
-                    # messes up start-of-week, is Saturday not Monday. So check
-                    # if same week another way
-                    dt_sub_i = last_main_dt
-                    fixed = True
-            elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval.endswith('m') or interval.endswith('h'):
-                td = _pd.to_timedelta(interval)
-                if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
-                    dt_sub_i = last_main_dt
-                    fixed = True
-        new_index[i] = dt_sub_i
+    new_index = df_main.index[indices]
    df_sub = _reindex_events(df_sub, new_index, data_col)
-    df = df_main.join(df_sub)

+    df = df_main.join(df_sub)
    f_na = df[data_col].isna()
    data_lost = sum(~f_na) < df_sub.shape[0]
    if data_lost:
-        ## Not always possible to match events with trading, e.g. when released pre-market.
-        ## So have to append to bottom with nan prices.
-        ## But should only be impossible with intra-day price data.
-        if interval.endswith('m') or interval.endswith('h') or interval == "1d":
-            # Update: is possible with daily data when dividend very recent
-            f_missing = ~df_sub.index.isin(df.index)
-            df_sub_missing = df_sub[f_missing].copy()
-            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
-                    "Close"}.intersection(df.columns)
-            df_sub_missing[list(keys)] = _np.nan
-            col_ordering = df.columns
-            df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
-        else:
-            raise Exception("Lost data during merge despite all attempts to align data (see above)")
+        raise Exception('Data was lost in merge, investigate')

    return df

@@ -752,7 +805,7 @@ def format_history_metadata(md, tradingPeriodsOnly=True):

    if "tradingPeriods" in md:
        tps = md["tradingPeriods"]
-        if tps == {"pre":[], "post":[]}:
+        if tps == {"pre": [], "post": []}:
            # Ignore
            pass
        elif isinstance(tps, (list, dict)):
@@ -768,8 +821,8 @@ def format_history_metadata(md, tradingPeriodsOnly=True):
                post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
                regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))

-                pre_df = pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
-                post_df = post_df.rename(columns={"start":"post_start", "end":"post_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                pre_df = pre_df.rename(columns={"start": "pre_start", "end": "pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                post_df = post_df.rename(columns={"start": "post_start", "end": "post_end"}).drop(["timezone", "gmtoffset"], axis=1)
                regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)

                cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
@@ -786,6 +839,7 @@ def format_history_metadata(md, tradingPeriodsOnly=True):

    return md

+
 class ProgressBar:
    def __init__(self, iterations, text='completed'):
        self.text = text
@@ -818,19 +872,16 @@ class ProgressBar:
    def update_iteration(self, val=None):
        val = val if val is not None else self.elapsed / float(self.iterations)
        self.__update_amount(val * 100.0)
-        self.prog_bar += '  %s of %s %s' % (
-            self.elapsed, self.iterations, self.text)
+        self.prog_bar += f"  {self.elapsed} of {self.iterations} {self.text}"

    def __update_amount(self, new_amount):
        percent_done = int(round((new_amount / 100.0) * 100.0))
        all_full = self.width - 2
        num_hashes = int(round((percent_done / 100.0) * all_full))
-        self.prog_bar = '[' + self.fill_char * \
-                        num_hashes + ' ' * (all_full - num_hashes) + ']'
+        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
        pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
-        pct_string = '%d%%' % percent_done
-        self.prog_bar = self.prog_bar[0:pct_place] + \
-                        (pct_string + self.prog_bar[pct_place + len(pct_string):])
+        pct_string = f'{percent_done}%%'
+        self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])

    def __str__(self):
        return str(self.prog_bar)
@@ -841,7 +892,7 @@ class ProgressBar:
 # ---------------------------------

 class _KVStore:
-    """Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
+    """Simple Sqlite backed key/value store, key and value are strings. Should be thread safe."""

    def __init__(self, filename):
        self._cache_mutex = Lock()
@@ -905,7 +956,10 @@ class _TzCache:
    def __init__(self):
        self._setup_cache_folder()
        # Must init db here, where is thread-safe
-        self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        try:
+            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        except _sqlite3.DatabaseError as err:
+            raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
        self._migrate_cache_tkr_tz()

    def _setup_cache_folder(self):
@@ -913,12 +967,10 @@ class _TzCache:
            try:
                _os.makedirs(self._db_dir)
            except OSError as err:
-                raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
-                                        .format(self._db_dir, err))
+                raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")

        elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
-            raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
-                                    .format(self._db_dir, ))
+            raise _TzCacheException(f"Cannot read and write in TzCache folder: '{self._db_dir}'")

    def lookup(self, tkr):
        return self.tz_db.get(tkr)
@@ -926,10 +978,14 @@ class _TzCache:
    def store(self, tkr, tz):
        if tz is None:
            self.tz_db.delete(tkr)
-        elif self.tz_db.get(tkr) is not None:
-            raise Exception("Tkr {} tz already in cache".format(tkr))
        else:
-            self.tz_db.set(tkr, tz)
+            tz_db = self.tz_db.get(tkr)
+            if tz_db is not None:
+                if tz != tz_db:
+                    get_yf_logger().debug(f'{tkr}: Overwriting cached TZ "{tz_db}" with different TZ "{tz}"')
+                    self.tz_db.set(tkr, tz)
+            else:
+                self.tz_db.set(tkr, tz)

    @property
    def _db_dir(self):
@@ -955,7 +1011,7 @@ class _TzCache:
        else:
            # Discard corrupt data:
            df = df[~df["Tz"].isna().to_numpy()]
-            df = df[~(df["Tz"]=='').to_numpy()]
+            df = df[~(df["Tz"] == '').to_numpy()]
            df = df[~df.index.isna()]
            if not df.empty:
                try:
@@ -994,10 +1050,9 @@ def get_tz_cache():
            try:
                _tz_cache = _TzCache()
            except _TzCacheException as err:
-                logger.error("Failed to create TzCache, reason: %s. "
-                             "TzCache will not be used. "
-                             "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'",
-                             err)
+                get_yf_logger().info(f"Failed to create TzCache, reason: {err}. "
+                                     "TzCache will not be used. "
+                                     "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
                _tz_cache = _TzCacheDummy()

        return _tz_cache
--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "0.2.19"
+version = "0.2.28"
Author	SHA1	Message	Date
Value Raider	0713d93867	Bump version to 0.2.28	2023-08-13 13:05:02 +01:00
Value Raider	67e81a8f9a	Fix TypeError: 'FastInfo' object is not callable #1636	2023-08-13 13:01:53 +01:00
ValueRaider	b6372c0945	Merge pull request #1661 from ranaroussi/dev sync dev -> main	2023-08-13 12:44:05 +01:00
ValueRaider	c9dd582dd8	Merge branch 'main' into dev	2023-08-13 12:40:11 +01:00
ValueRaider	677f3d5702	Merge pull request #1660 from ranaroussi/fix/price-repair-100x-and-calibration Fix price repair: 100x & calibration	2023-08-13 12:21:55 +01:00
Value Raider	4f9b05a546	Fix price repair: 100x & calibration Price repair fixes and improvement Fixes: - fix reconstruction mis-calibration with tiny DataFrames - fix detecting last-active-trading-interval when NaNs in DataFrame - redesign mapping 100x signals to ranges: - no change for signals before last-active-trading-interval - but for signals after last-active-trading-interval, process in reverse order Improvements: - increase max reconstruction depth from 1 to 2. E.g. now 1wk can be repaired with 1h (1wk->1d->1h)	2023-08-13 10:54:57 +01:00
ValueRaider	e1f94ed337	Merge pull request #1654 from ranaroussi/fix/circular-import Fix circular import in utils.py	2023-08-05 12:56:15 +01:00
Value Raider	93a7ee6161	Fix circular import in utils.py Commit a4d7d6 introduced a circular import into utils.py	2023-08-05 12:54:12 +01:00
Value Raider	5b0cb60cf5	Bump version to 0.2.27	2023-08-03 21:24:07 +01:00
ValueRaider	1a97c22874	Merge pull request #1635 from ranaroussi/hotfix/prices-events-merge Fix merging 1d-prices with out-of-range divs/splits	2023-08-03 21:20:29 +01:00
ValueRaider	b0de31da63	Merge pull request #1648 from ranaroussi/hotfix/tkr-tz-already-in-cache Fix multithread error 'tz already in cache'	2023-08-03 18:02:51 +01:00
Value Raider	cc87608824	Fix multithread error 'tz already in cache'	2023-08-02 19:29:06 +01:00
ValueRaider	6c1e26093c	Merge pull request #1633 from ranaroussi/feature/price-repair-improvements Improve price repair	2023-08-01 20:03:29 +01:00
ValueRaider	e8fdd12cb1	Merge branch 'dev' into feature/price-repair-improvements	2023-07-31 18:30:00 +01:00
Value Raider	93b6e024da	Improve bad-split-repair on multiday intervals Improve bad-split-repair on multiday intervals Switch some repair log msgs from warning -> info	2023-07-31 18:19:42 +01:00
ValueRaider	d5282967ce	Merge pull request #1606 from DanielGoldfarb/main option_chain() return underlying data that comes with the options data	2023-07-27 15:10:13 +01:00
ValueRaider	9908c1ff48	Merge pull request #1638 from ricardoprins/dev PEP 8 changes + minor performance improvements	2023-07-26 17:49:25 +01:00
Ricardo Prins	a4d7d6c577	PEP 8 changes + minor performance improvements	2023-07-25 21:01:46 -06:00
ValueRaider	f9080c22a5	Merge pull request #1630 from ricardoprins/dev Adjust PEP 8 + minor improvements + f-strings in base.py	2023-07-25 16:50:01 +01:00
Ricardo Prins	32e1d479b1	Remove last _pd	2023-07-23 18:37:09 -06:00
Ricardo Prins	5729ce3cb6	Remove .formats and adjust imports	2023-07-23 18:28:30 -06:00
ValueRaider	d0b2070036	Fix merging 1d-prices with out-of-range divs/splits	2023-07-23 15:20:57 +01:00
ValueRaider	688120cab7	Merge pull request #1632 from ranaroussi/fix/history-30m-typo Fix typo in Ticker.history(30m)	2023-07-23 13:18:35 +01:00
ValueRaider	4a1e1c4447	Merge branch 'dev' into feature/price-repair-improvements	2023-07-22 20:34:56 +01:00
ValueRaider	f99677ed1e	Fix typo in Ticker.history(30m)	2023-07-22 16:52:50 +01:00
ValueRaider	6a613eb114	Improve price repair Several improvements to price repair Repair 100x and split errors: - Handle stocks that recently suspended - use latest ACTIVE trading as baseline - Improve error identification: - Restrict repair to no older than 1 year before oldest split - To reduce false positives when checking for multiday split errors, only analyse 'Open' and 'Close' and use average change instead of nearest-to-1 - For weekly intervals reduce threshold to 3x standard deviation (5x was too high), and for monthly increase to 6x - For multiday intervas, if errors only detected in 1 column then assume false positive => ignore Repair missing div-adjust: - Fix repair of multiday intervals containing dividend Price reconstruction: - Move to after repairing 100x and split errors, so calibration works properly - Fix maximum depth and reduce to 1 - Restrict calibration to 'Open' and 'Close', because 'Low' and 'High' can differ significantly between e.g. 1d and day-of-1h Miscellaneous: - Deprecate repair='silent', the logging module handles this - Improve tests for 100x and split errors - New test for 'repair missing div adjust'	2023-07-22 15:09:38 +01:00
Ricardo Prins	0503240973	Adjust PEP 8 + minor improvements + f-strings	2023-07-21 20:33:47 -06:00
ValueRaider	ae6c05fa74	Merge pull request #1628 from ricardoprins/dev Adjust PEP 8 + minor improvement	2023-07-21 22:35:21 +01:00
Ricardo Prins	aa9a0286a1	Add missing comment to test_dailyWithEvents	2023-07-21 14:25:46 -06:00
ValueRaider	ddf0cf19cd	Bump version to 0.2.26	2023-07-21 12:56:10 +01:00
Ricardo Prins	a2bde88c36	Adjust PEP 8 + minor improvement	2023-07-20 22:44:36 -06:00
ValueRaider	1bd819ac4d	Merge pull request #1371 from ranaroussi/hotfix/proxy Fix proxy arg passthrough	2023-07-21 01:01:14 +01:00
ValueRaider	1b9fc5f12f	Merge pull request #1625 from ricardoprins/main Bump requests to 2.31 and removes cryptography.	2023-07-21 00:59:46 +01:00
Ricardo Prins	274f309052	Bump requests to 2.31 and removes cryptography.	2023-07-20 17:17:44 -06:00
ValueRaider	edac283a60	Merge pull request #1623 from ranaroussi/bug-report-yaml Fix yaml issue rendering	2023-07-19 18:21:52 +01:00
ValueRaider	781fad501f	Merge branch 'main' into bug-report-yaml	2023-07-19 18:21:44 +01:00
ValueRaider	39527d24d4	Fix yaml issue template rendering	2023-07-19 18:21:04 +01:00
ValueRaider	45f1c88460	yaml issue template - escape some backticks	2023-07-19 18:09:20 +01:00
ValueRaider	7d638e1040	Merge pull request #1613 from ranaroussi/bug-report-yaml Convert issue template to yaml	2023-07-19 18:05:40 +01:00
ValueRaider	97b13dfa8c	Convert issue template to yaml + improve	2023-07-19 18:01:47 +01:00
ValueRaider	693565a85b	Bump version to 0.2.25	2023-07-18 13:45:55 +01:00
ValueRaider	957051e0e8	Merge pull request #1605 from ranaroussi/dev sync dev -> main	2023-07-18 12:02:19 +01:00
ValueRaider	bd81ebb4e9	Merge pull request #1611 from ricardoprins/main [BUG] Fix failure when using single ISIN as a ticker (#1525)	2023-07-18 10:55:18 +01:00
ValueRaider	46f53f9957	Port proxy fix to relocated 'FastInfo'	2023-07-17 18:34:00 +01:00
ValueRaider	056b84d8fe	Merge branch 'main' into hotfix/proxy	2023-07-17 18:29:04 +01:00
Ricardo Prins	835dbd9629	Fix failure when using single ISIN as a ticker	2023-07-17 08:49:39 -06:00
ValueRaider	07a4594455	Dev version 0.2.25b1	2023-07-14 21:55:29 +01:00
Daniel Goldfarb	736c03ac5b	options_chain() return underlying data that comes with the options data	2023-07-14 15:17:17 -04:00
ValueRaider	adfa2e9beb	Merge pull request #1604 from ranaroussi/main sync main -> dev	2023-07-14 20:11:45 +01:00
ValueRaider	b286797e8c	Bump version to 0.2.24	2023-07-14 15:52:33 +01:00
ValueRaider	b306bef350	Merge pull request #1603 from ranaroussi/hotfix/info-missing-values Fix info[] missing values	2023-07-14 15:51:23 +01:00
ValueRaider	61c89660df	Optimise info fetch, improve test	2023-07-14 15:29:55 +01:00
Value Raider	31af2ab1d5	Fix recently-fixed info[] missing data	2023-07-13 22:20:42 +01:00
Value Raider	21c380fa61	Bump version to 0.2.23	2023-07-13 20:54:56 +01:00
ValueRaider	e0000cd787	Merge pull request #1595 from signifer-geo/bug20230714 Update quote.py	2023-07-13 20:51:33 +01:00
signifer-geo	11d43eb1a1	Update quote.py dead code deleted	2023-07-14 04:29:59 +09:00
signifer-geo	509a109f29	Update quote.py It fixes the error: unauthorized, invalid crumb	2023-07-14 03:11:24 +09:00
ValueRaider	b0639409a3	Merge pull request #1586 from ranaroussi/improve-readme Emphasise API on Wiki	2023-07-10 15:26:31 +01:00
ValueRaider	ed10feee9a	Merge pull request #1584 from lucas03/lukas/start-date-docs update start parameter docstring	2023-07-06 21:20:17 +01:00
ValueRaider	aba81eedc2	Emphasise API on Wiki More emphasis that user should review the Wiki for the full API for download() and Ticker.history()	2023-07-06 21:15:41 +01:00
Lukas Vojt	d424d027ac	update docstrings for start parameter requested here https://github.com/ranaroussi/yfinance/pull/1576#issuecomment-1616599633	2023-07-06 08:17:07 +00:00
ValueRaider	9268fcfa76	Merge pull request #1545 from SnoozeFreddo/main fix: Readme cache-ratelimit. Limiter parenthesis was never closed	2023-06-27 13:54:08 +01:00
ValueRaider	711e1138d3	Merge pull request #1576 from lucas03/lukas/start-date fix start date on history	2023-06-27 12:32:17 +01:00
Lukas Vojt	0789b690a4	fix: start year on history timestamp of 1900 is older than 100 years, so yahoo responds with error: GDEVW: 1d data not available for startTime=-2208994789 and endTime=1687780922. Only 100 years worth of day granularity data are allowed to be fetched per request. this should fix it, something similar was proposed here: https://github.com/ranaroussi/yfinance/pull/648 # Please enter the commit message for your changes. Lines starting	2023-06-26 18:43:35 +02:00
ValueRaider	6055566de8	Bump version to 0.2.22	2023-06-24 19:42:18 +01:00
ValueRaider	398a19a855	Merge pull request #1574 from ranaroussi/hotfix/sql-db-error Fix unhandled 'sqlite3.DatabaseError'	2023-06-24 19:40:51 +01:00
ValueRaider	e771cfabb6	Fix unhandled 'sqlite3.DatabaseError' ... also move '_TzCacheException' logging from level ERROR to INFO, because users don't need to know	2023-06-24 16:57:06 +01:00
ValueRaider	5b676f803b	Bump version to 0.2.21	2023-06-21 23:15:37 +01:00
ValueRaider	eb5c50d5c7	Merge pull request #1569 from ranaroussi/dev sync dev -> main	2023-06-21 23:13:49 +01:00
ValueRaider	1cb0b215c4	Merge branch 'main' into dev	2023-06-21 23:13:12 +01:00
ValueRaider	50dcb2ce5a	Merge pull request #1568 from ranaroussi/fix/financials Fix financials tables	2023-06-21 23:07:50 +01:00
ValueRaider	1ce9ce2784	Fix financials ; Remove broken decryption & scraping	2023-06-21 14:49:16 +01:00
ValueRaider	cd4816e289	Post-merge tidy price-repair logging	2023-06-20 17:46:06 +01:00
ValueRaider	27e9ce7542	Merge pull request #1543 from ranaroussi/feature/fix-Yahoo-bad-div-split-adjustments Price repair update: fix Yahoo messing up dividend and split adjustments	2023-06-20 17:41:16 +01:00
ValueRaider	02c1c60f3b	Merge branch 'dev' into feature/fix-Yahoo-bad-div-split-adjustments	2023-06-20 17:39:19 +01:00
ValueRaider	27ea9472c1	Price repair improvements Improve split-repair of multi-day intervals. Because split error can occur within a multi-day interval, e.g. mid-way through week, need to repair each OHLC column separately Increase robustness of repair 'Adj Close' Limit price-repair recursion depth to 2	2023-06-20 17:37:19 +01:00
ValueRaider	801f58790a	Merge pull request #1562 from ranaroussi/fix/logging-behaviour Fix logging behaviour	2023-06-19 23:26:14 +01:00
ValueRaider	080834e3ce	Update README#logging ; remove 'info fixed' message	2023-06-19 23:25:36 +01:00
ValueRaider	4e7b2094d0	Logging: improve appearance, fix propagation Various important changes to yfinance logging: Remove handler from YF logger as breaks standard logging practice. Improve 'download' log message grouping Move custom DEBUG log formatting into 'yf.enable_debug_mode()', which if called adds the handler. The custom DEBUG log formatting is: - 'MultiLineFormatter' improves appearance of multi-line messages in any logging mode. Adds leading indent to all lines below first, to align with the first line which is indented by %levelname%. - Whitespace padding is added to end of %levelname% string up to 8 characters. This resolves different level strings e.g. INFO & DEBUG creating different indents. But this only automatically applies to first line of message - for multi-line messages, 'MultiLineFormatter' extracts amount of padding and applies to all other lines. - Add leading indent proportional to function depth, because DEBUG generates a lot of messages particularly when repairing price data - same function can be called recursively. Implemented in 'IndentLoggerAdapter', a simple wrapper around the logger object. - 'log_indent_decorator' inserts 'Entering/Exiting %function%' debug messages, helps organise.	2023-06-19 19:42:02 +01:00
ValueRaider	c72e04bf55	Merge pull request #1567 from ranaroussi/fix/future-event-merge Fix merge future div/split into prices	2023-06-19 11:56:48 +01:00
ValueRaider	abbe4c3a2f	Fix merge future div/split into prices	2023-06-19 11:41:53 +01:00
ValueRaider	9e21b85043	Merge branch 'dev' into feature/fix-Yahoo-bad-div-split-adjustments	2023-06-17 12:49:01 +01:00
ValueRaider	b44917b7f9	Improve price-repair of 1d 'Adj Close' Main change is fixing price-repair of 1d 'Adj Close'. 1d repair uses 1h data, but 1h is never div-adjusted. For correct 1d 'Adj Close', extract div-adjustment from the good 1d data, and calculate it for any bad 1d data. A new unit test ensures correctness. Other changes: - bug fix in split-repair logic to handle price=0 - improve unit test coverage on price dividend - add 1wk interval to split-repair unit test	2023-06-16 14:06:25 +01:00
ValueRaider	6f78dd6e6b	Fix dumb bug	2023-06-12 17:05:37 +01:00
ValueRaider	593dc8fcee	Use new split-repair logic to also fix 100x error Stumbled upon another type of 100x price error - Yahoo may switch a symbol from e.g. cents -> $ on some recent date, so recent prices are 100x different. The new split-repair is perfect for this - set change to 100 and ignore 'Volume'.	2023-06-12 14:46:58 +01:00
ValueRaider	b94baa4cc5	Merge pull request #1552 from adityanparikh/fix/timedelta Fix timedelta bug	2023-06-08 21:26:31 +01:00
ValueRaider	1a054135fb	Merge pull request #1553 from ranaroussi/fix/events-merging Fix prices-events merge	2023-06-08 21:18:14 +01:00
ValueRaider	4e2253a406	Fix prices-events merge An out-of-range dividend was breaking merge with 1mo-prices, so fixed that. Also replaced the mega-loop with Numpy, much clearer now. Improved its tests.	2023-06-08 21:16:05 +01:00
Aditya Parikh	9af7ec0a4e	Fix timedelta bug Fix for exception using _pd.Timedelta function with unit='d' parameter for Pandas v1.4.4	2023-06-08 16:15:03 -04:00
Value Raider	8624216e21	Bump version to 0.2.20	2023-06-07 16:51:17 +01:00
ValueRaider	954e71d19c	Update action versions in python-publish.yml Recent release action generated deprecated error: "Node.js 12 actions are deprecated. Please update the following actions to use Node.js 16: actions/checkout@v2, actions/setup-python@v2." So simply increasing versions to match latest GitHub usage docs, hopefully works.	2023-06-07 16:48:11 +01:00
ValueRaider	e57647c1d7	Stock split repair: bug fixes & more testing	2023-06-03 20:49:35 +01:00
Konstantinos Ftikas	762abd8bba	fix: Readme cache-ratelimit. Limiter parenthesis was never closed The example in the docs will not work out of the box due to a syntax error.	2023-06-03 14:19:31 +02:00
ValueRaider	d1ea402792	Price repair improvement: fix stupid bug	2023-06-01 15:56:54 +01:00
ValueRaider	65f65b1776	Price repair improvement: fix price Low=High errors on stock split day Yahoo often messes up price data on stock split day - all equal or simply missing. Impossible that price didn't move/trade on stock split day.	2023-05-31 21:51:04 +01:00
ValueRaider	9388c29207	Price repair improvement: fix stock split adjustment missing from pre-split data	2023-05-31 21:41:05 +01:00
ValueRaider	9f91f4b180	Price repair improvement: fix missing dividend adjustment	2023-05-31 21:40:16 +01:00
ValueRaider	f8aab533ba	Merge branch 'main' into hotfix/proxy	2023-02-08 13:52:34 +00:00
ValueRaider	5cdc78f479	Merge pull request #1398 from vidalmarco/patch-1 get_shares_full does not work with proxy	2023-02-05 10:59:33 +00:00
Marco Vidal	ba634fad0e	get_shares_full does not work with proxy Error: "Yahoo web request for share count failed" updated cache_get call by adding proxy parameter and by calling it by keyword	2023-02-05 09:17:22 +01:00
ValueRaider	8a5ca71f52	Fix holders.py proxy pass-through	2023-02-05 00:06:49 +00:00
ValueRaider	141ce7e471	Fix proxy + cache_get. Improve error propagation	2023-02-01 21:19:54 +00:00
ValueRaider	4eae728a06	Potential fix for proxy - enable #2	2023-02-01 19:17:18 +00:00
ValueRaider	2d6b6b26ed	Potential fix for proxy - enable	2023-02-01 19:04:47 +00:00
ValueRaider	ec3dfaf305	Potential fix for proxy - revert	2023-02-01 18:10:45 +00:00
ValueRaider	e89d390824	Potential fix for proxy	2023-02-01 18:09:51 +00:00
ValueRaider	563a1a3448	Add Ticker test for proxy	2023-02-01 17:28:57 +00:00
ValueRaider	2e6d3d0e60	Fix proxy in 'history()'	2023-02-01 17:06:23 +00:00
ValueRaider	553bc5965a	Fix proxy arg passthrough	2023-01-28 23:07:19 +00:00